Centre for Internet and Society

Database on Big Data and Smart Cities International Standards

vanya — 2016-02-11T15:49:45Z

The Centre for Internet and Society is in the process of mapping international standards specifically around Big Data, IoT and Smart Cities. Here is a living document containing a database of some of these key globally accepted standards.

1. International Organisation for Standardization: ISO/IEC JTC 1 Working group on Big Data (WG 9 )

● Background

- The International Organization for Standardization /International Electrotechnical Commission (ISO/IEC) Joint Technical Committee (JTC) 1, Information Technology announced the creation of a Working Group (WG) focused on standardization in connection with big data.

- JTC 1 is the standards development environment where experts come together to develop worldwide standards on Information and Communication Technology (ICT) for integrating diverse and complex ICT technologies.^{^[1]}

- The American National Standards Institute (ANSI) holds the secretariat to JTC 1 and the ANSI-accredited U.S. Technical Advisory Group (TAG) Administrator to JTC 1 is theInterNational Committee for Information Technology Standards (INCITS) ^{^[2]}, an ANSI member and accredited standards developer (ASD). InterNational Committee for Information Technology standards (INCITS) is a technical committee on Big Data to serve as the US Technical Advisory Group (TAG) to JTC 1/WG 9 on Big Data/ pending approval of a New Work Item Proposal (NWIP). The INCITS/Big Data will address standardization in the areas assigned to JTC 1/WG 9. ^{^[3]}

- Under U.S. leadership, WG 9 on Big Data will serve as the focus of JTC 1's big data standardization program.

● Objective

- To identify standardization gaps.

- Develop foundational standards for Big Data.

- Develop and maintain liaisons with all relevant JTC 1 entities

- Grow the awareness of and encourage engagement in JTC 1 Big Data standardization efforts within JTC 1. ^{^[4]}

● Status

- JTC 1 appoints Mr. Wo Chang to serve as Convenor of the JTC 1 Working Group on Big Data.

- The WG has set up a Study Group on Big Data.

2. International Organisation for Standardization: ISO/IEC JTC 1 Study group on Big Data

● Background

- The ISO/IEC JTC1 Study Group on Big Data (JTC1 SGBD) was created by Resolution 27 at the November, 2013 JTC1 Plenary at the request of the USA and other national bodies for consideration of Big Data activities across all of JTC 1.

- A Study Group (SG) is an ISO mechanism by which the convener of a Working Group (WG) under a sub-committee appoints a smaller group of experts to do focused work in a specific area to identify a clear group to focus attention on a major area and expand the manpower of the committee.

- The goal of an SG is to create a proposal suitable for consideration by the whole WG, and it is the WG that will then decide whether and how to progress the work.^{^[5]}

● Objective

JTC 1 establishes a Study Group on Big Data for consideration of Big Data

activities across all of JTC 1 with the following objectives:

- Mapping the existing landscape: Map existing ICT landscape for key technologies and relevant standards /models/studies /use cases and scenarios for Big Data from JTC 1, ISO, IEC and other standards setting organizations,

- Identify key terms : Identify key terms and definitions commonly used in the area of Big Data,

- Assess status of big data standardization : Assess the current status of Big Data standardization market requirements, identify standards gaps, and propose standardization priorities to serve as a basis for future JTC 1 work, and

- Provide a report with recommendations and other potential deliverables to the 2014 JTC 1 Plenary. ^{^[6]}

● Current Status

- The study group released a preliminary report in the year 2014, which can be accessed here : http://www.iso.org/iso/big_data_report-jtc1.pdf.

3. The National Institute of Standards and Technology Big Data Interoperability Framework :

● Background

- NIST is leading the development of a Big Data Technology Roadmap which aims to define and prioritize requirements for interoperability, portability, reusability, and extensibility for big data analytic techniques and technology infrastructure to support secure and effective adoption of Big Data.

- To help develop the ideas in the Big Data Technology Roadmap, NIST is creating the Public Working Group for Big Data which Released Seven Volumes of Big Data Interoperability Framework on September 16, 2015.^{^[7]}

● Objective

- To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data.

● Status

- The results are reported in the NIST Big Data Interoperability Framework series of volumes. Under the framework, seven volumes have been released by NIST, available here:

http://bigdatawg.nist.gov/V1_output_docs.php

4. IEEE Standards Association

● Background:

- The IEEE Standards Association introduced a number of standards

related to big-data applications.

● Status:

The following standard is under development:

- IEEE P2413

"IEEE Standard for an Architectural Framework for the Internet of Things (IoT)" defines the relationships among devices used in industries, including transportation and health care. It also provides a blueprint for data privacy, protection, safety, and security, as well as a means to document and mitigate architecture divergence.^{^[8]}

5. ITU

● Background:

- The International Telecommunications Union (ITU) has announced its first standards for big data services, entitled 'Recommendation ITU-T Y.3600 "Big data - cloud computing based requirements and capabilities"', recognizing the need for strong technical standards considering the growth of big data to ensure that processing tools are able to achieve powerful results in the areas of collection, analysis, visualization, and more.^{^[9]}

● Objective:

- Recommendation Y.3600 provides requirements, capabilities and use cases of

cloud computing based big data as well as its system context. Cloud computing

based big data provides the capabilities to collect, store, analyze, visualize and

manage varieties of large volume datasets, which cannot be rapidly transferred

and analysed using traditional technologies.^{^[10]}

- It also outlines how cloud computing systems can be leveraged to provide big-data services.

● Status:

- The standard was relseased in the year 2015 and is avaiabe here: http://www.itu.int/rec/T-REC-Y.3600-201511-I .

Smart cities

1. ISO Standards on Smart Cities

● Background:

- ISO, the International Organization for Standardization, established a strategic advisory group in 2014 for smart cities, comprised of a wide range of international experts to advise ISO on how to coordinate current and future Smart City standardization activities, in cooperation with other international standards organizations, to benefit the market.^{^[11]}

- Seven countries, China, Germany, UK, France, Japan, Korea and USA, are currently involved in the research.

● Objective:

- The main aims of which are to formulate a definition of a Smart City

- Identify current and future ISO standards projects relating to Smart Cities

- Examine involvement of potential stakeholders, city requirements, potential interface problems. ^{^[12]}

● Status:

- ISO/TC 268, which is focused on sustainable development in communities, has one working group developing city indicators and other developing metrics for smart community infrastructures. In early 2016 this committee will be joined by another - IEC - systems committee. The first standard produced by ISO/TC 268 is ISO/TR 37150:2014.

- ISO/TR 37150:2014 Smart community infrastructures -- Review of existing activities relevant to metrics: this standard provides a review of existing activities relevant to metrics for smart community infrastructures. The concept of smartness is addressed in terms of performance relevant to technologically implementable solutions, in accordance with sustainable development and resilience of communities, as defined in ISO/TC 268. ISO/TR 37150:2014 addresses community infrastructures such as energy, water, transportation, waste and information and communications technology (ICT). It focuses on the technical aspects of existing activities which have been published, implemented or discussed. Economic, political or societal aspects are not analyzed in ISO/TR 37150:2014.^{^[13]}

- ISO 37120:2014 provides city leaders and citizens a set of clearly defined city performance indicators and a standard approach for measuring each. Though some indicators will be more helpful for cities than others, cities can now consistently apply these indicators and accurately benchmark their city services and quality of life against other cities.^{^[14]} This new international standard was developed using the framework of the Global City Indicators Facility (GCIF) that has been extensively tested by more than 255 cities worldwide. This is a demand-led standard, driven and created by cities, for cities. ISO 37120 defines and establishes definitions and methodologies for a set of indicators to steer and measure the performance of city services and quality of life. The standard includes a comprehensive set of 100 indicators - of which 46 are core - that measures a city's social, economic, and environmental performance. ^{^[15]}

The GCIF global network, supports the newly constituted World Council on City Data - a sister organization of the GCI/GCIF - which allows for independent, third party verification of ISO 37120 data.^{^[16]}

- ISO/TS 37151 and ISO/TR 37152 Smart community infrastructures -- Common framework for development & operation: outlines 14 categories of basic community needs (from the perspective of residents, city managers and the environment) to measure the performance of smart community infrastructures. These are typical community infrastructures like energy, water, transportation, waste and information and communication technology systems, which have been optimized with sustainable development and resilience in mind. ^{^[17]} The committee responsible for this document is ISO/TC 268, Sustainable development in communities, Subcommittee SC 1, Smart community infrastructures. The objective is to develop international consensus on a harmonised metrics to evaluate the smartness of key urban infrastructure.^{^[18]}

- ISO 37101 Sustainable development of communities -- Management systems -- Requirements with guidance for resilience and smartness : By setting out requirements and guidance to attain sustainability with the support of methods and tools including smartness and resilience, it can help communities improve in a number of areas such as: Developing holistic and integrated approaches instead of working in silos (which can hinder sustainability), Fostering social and environmental changes, Improving health and wellbeing, Encouraging responsible resource use and Achieving better governance. ^{^[19]} The objective is to develop a Management System Requirements Standard reflecting consensus on an integrated, cross-sector approach drawing on existing standards and best practices.

- ISO 37102 Sustainable development & resilience of communities - Vocabulary . The objective is to establish a common set of terms and definitions for standardization in sustainable development, resilience and smartness in communities, cities and territories since there is pressing need for harmonization and clarification. This would provide a common language for all interested parties and stakeholders at the national, regional and international levels and would lead to improved ability to conduct benchmarks and to share experiences and best practices.

- ISO/TR 37121 Inventory & review of existing indicators on sustainable development & resilience in cities : A common set of indicators useable by every city in the world and covering most issues related to sustainability, resilience and quality of life in cities. ^{^[20]}

- ISO/TR 12859:2009 gives general guidelines to developers of intelligent transport systems (ITS) standards and systems on data privacy aspects and associated legislative requirements for the development and revision of ITS standards and systems. ^{^[21]}

2. International Organisation for Standardization: ISO/IEC JTC 1 Working group on Smart Cities (WG 11 )

● Background:

- Serve as the focus of and proponent for JTC 1's Smart Cities standardization program and works for development of foundational standards for the use of ICT in Smart Cities - including the Smart City ICT Reference Framework and an Upper Level Ontology for Smart Cities - for guiding Smart Cities efforts throughout JTC 1 upon which other standards can be developed.^{^[22]}

● Objective:

- To develop a set of ICT related indicators for Smart Cities in collaboration with ISO/TC 268.

- Identify JTC 1 (and other organization) subgroups developing standards and related material that contribute to Smart Cities.

- Grow the awareness of, and encourage engagement in, JTC 1 Smart Cities standardization efforts within JTC 1.

● Status

- Ms Yuan Yuan is the Convenor of this Working group.

- The purpose was to provide a report with recommendations to the JTC 1 Plenary in the year 2014, to which a preliminary report was submitted. ^{^[23]}

3. International Organisation for Standardization: ISO/IEC JTC 1 Study Group (SG1) on Smart Cities

● Background:

- The Study Group (SG) - Smart Cities was established in 2013^{^[24]} SG 1 will explicitly consider the work going on in the following committees: ISO/TMB/AG on Smart Cities, IEC/SEG 1, ITU-T/FG SSC and ISO/TC 268. ^{^[25]}

● Objective :

- To examine the needs and potentials for standardization in this area.

● Status:

- SG 1 is paying particular attention to monitoring cloud computing activities, which it sees as the key element of the Smart Cities infrastructure. DIN's Information Technology and Selected IT Applications Standards Committee (NIA (www.nia.din.de)) is formally responsible for ISO/IEC JTC1 /SG 1, but an autonomous national mirror committee on Smart Cities does not yet exist and the work is being overseen by DIN's Smart Grid steering body. ^{^[26]}

- A preliminary report has been released in the 2014, available here- http://www.iso.org/iso/smart_cities_report-jtc1.pdf

4. ITU

● Background:

- ITU members have established an ITU-T Study Group titled "ITU-T Study Group 20: IoT and its applications, including smart cities and communities" ^{^[27]}

- ITU-T has also established a Focus Group on Smart Sustainable Cities (FG-SSC).

● Objective:

- The study group will address the standardization requirements of Internet of Things (IoT) technologies, with an initial focus on IoT applications in smart cities.

- The focus group shall assess the standardization requirements of cities aiming to boost their social, economic and environmental sustainability through the integration of information and communication technologies (ICTs) in their infrastructures and operations.

- The Focus Group will act as an open platform for smart-city stakeholders - such as municipalities; academic and research institutes; non-governmental organizations (NGOs); and ICT organizations, industry forums and consortia - to exchange knowledge in the interests of identifying the standardized frameworks needed to support the integration of ICT services in smart cities.^{^[28]}

● Status:

- The study group will develop standards that leverage IoT technologies to address urban-development challenges.

- The FG-SSC concluded its work in May 2015 by approving 21 Technical Specifications and Reports. ^{^[29]}

- So far, ITU-T SG 5 FG-SSC has issued the following reports- Technical report "An overview of smart sustainable cities and the role of information and communication technologies", Technical report "Smart sustainable cities: an analysis of definitions", Technical report "Electromagnetic field (EMF) considerations in smart sustainable cities", Technical specifications "Overview of key performance indicators in smart sustainable cities", Technical report "Smart water management in cities".^{^[30]}

5. PRIPARE Project :

● Background:

- The 7001 - PRIPARE Smart City Strategy is to to ensure that ICT solutions integrated in EIP smart cities will be compliant with future privacy regulation.

- PRIPARE aims to develop a privacy and security-by-design software and systems engineering methodology, using the combined expertise of the research community and taking into account multiple viewpoints (advocacy, legal, engineering, business).

● Objective:

- The mission of PRIPARE is to facilitate the application of a privacy and security-by-design methodology that will contribute to the advent of unhindered usage of Internet against disruptions, censorship and surveillance, support its practice by the ICT research community to prepare for industry practice and foster risk management culture through educational material targeted to a diversity of stakeholders.

● Status:

- Liaison is currently on-going so that it becomes a standard (OASIS and ISO).^{^[31]}

6. BSI-UK

● Background:

- In the UK, the British Standards Institution (BSI) has been commissioned by the UK Department of Business, Innovation and Skills (BIS) to conceive a Smart Cities Standards Strategy to identify vectors of smart city development where standards are needed.

- The standards would be developed through a consensus-driven process under the BSI to ensure good practise is shared between all the actors. ^{^[32]}

● Objective:

The BIS launched the City's Standards Institute to bring together cities and key

industry leaders and innovators :

- To work together in identifying the challenges facing cities,

- Providing solutions to common problems, and

- Defining the future of smart city standards.^{^[33]}

● Status:

The following standards and publications help address various issues for a city to

become a smart city:

- The development of a standard on Smart city terminology (PAS 180)

- The development of a Smart city framework standard (PAS 181)

- The development of a Data concept model for smart cities (PAS 182)

- A Smart city overview document (PD 8100)

- A Smart city planning guidelines document (PD 8101)

- BS 8904 Guidance for community sustainable development provides a decision-making framework that will help setting objectives in response to the needs and aspirations of city stakeholders

- BS 11000 Collaborative relationship management

- BSI BIP 2228:2013 Inclusive urban design - A guide to creating accessible public spaces.

7. Spain

● Background:

- AENOR, the Spanish standards developing organization (SDO), has issued two new standards on smart cities: the UNE 178303 and UNE-ISO 37120. These standards joined the already published UNE 178301.

● Objective:

- The texts, prepared by the Technical Committee of Standardization of AENOR on Smart Cities (AEN / CTN 178) and sponsored by the SETSI (Secretary of State for Telecommunications and Information Society of the Ministry of Industry, Energy and Tourism), aim to encourage the development of a new model of urban services management based on efficiency and sustainability.

● Status:

Some of the standards that have been developed are:

- UNE 178301 on Open Data evaluates the maturity of open data created or held by the public sector so that its reuse is provided in the field of Smart Cities.

- UNE 178303 establishes the requirements for proper management of municipal assets.

- UNE-ISO 37120 which collects the international urban sustainability indicators.

- Following the publication of these standards, 12 other draft standards on Smart Cities have just been made public, most of them corresponding to public services such as water, electricity and telecommunications, and multiservice city networks. ^{^[34]}

8. China

● Background:

Several national standardization committees and consortia have started

standardization work on Smart Cities, including:

- China National IT Standardization TC (NITS),

- China National CT Standardization TC,

- China National Intelligent Transportation System Standardization TC,

- China National TC on Digital Technique of Intelligent Building and Residence Community of Standardization Administration, China Strategic Alliance of Smart City Industrial Technology Innovation^{^[35]}

● Objective:

- In the year 2014, all the ministries involved in building smart cities in China joined with the Standardization Administration of China to create working groups whose job is to manage and standardize smart city development, though their activities have not been publicized. ^{^[36]}

● Status:

- China will continue to promote international standards in building smart cities and improve the competitiveness of its related industries in global market.

- Also, China's Standardization Administration has joined hands with National Development and Reform Commission, Ministry of Housing and Urban-Rural Development and Ministry of Industry and Information Technology in establishing and implementing standards for smart cities.

- When building smart cities, the country will adhere to the ISO 37120 and by the year 2020, China will establish 50 national standards on smart cities. ^{^[37]}

9. Germany

● Background :

- Member of European Innovation Partnership (EIP) for Smart Cities and Communities DKE (German Commission for Electrical, Electronic & Information Technologies) and DIN (GermanInstitute for Standardization) have developed a joint roadmap and Smart Cities recommendations for action in Germany.

● Objective:

- Its purpose is to highlight the need for standards and to serve as a strategic template for national and international standardization work in the field of smart city technology.

- The Standardization Roadmap highlights the main activities required to create smart cities. ^{^[38]}

● Status:

- An updated version of the standardization roadmap was released in the year 2015. ^{^[39]}

10. Poland

● Background:

- A coordination group on Smart and Sustainable Cities and Communities (SSCC) was set up in the beginning of 2014 to monitor any national standardization activities.

● Objective:

- It was decided to put forward a proposal to form a group at the Polish Committee for Standardization (PKN) providing recommendations for smart sustainable city standardization in Poland.

● Status:

It has two thematic groups:

- GT 1-2 on terminology and Technical Bodies in PKN Its scope covers a collection of English terms and their Polish equivalents related to smart and sustainable development of cities and communities to allow better communication among various smart city stakeholders. This includes the preparation of the list of Technical Bodies (OT) in PKN involved in standardization activities related to specific aspects of smart and sustainable local development and making proposals concerning the allocation of standardization works to the relevant OT in PKN.

- GT 3 for gathering information and the development and implementation of a work programme Its scope includes identifying stakeholders in Poland, and gathering information on any national "smart city" initiatives having an impact on environment-friendly development, sustainability, and liveability of a city. The group is also tasked with developing a work programme for GZ 1 based on identified priorities for Poland. Finally, its aim is to conduct communication and dissemination of activities to make the results of GZ 1 visible. ^{^[40]}

11. Europe

● Background:

- In 2012, the European standardization organizations CEN and CENELEC founded the Smart and Sustainable Cities and Communities Coordination Group (SSCC-CG), which is a Coordination Group established to coordinate standardization activities and foster collaboration around standardization work. ^{^[41]}

● Objective:

- The aim of the CEN-CENELEC-ETSI (SSCC-CG) is to coordinate and promote European standardization activities relating to Smart Cities and to advise the CEN and CENELEC (Technical) and ETSI Boards on standardization activities in the field of Smart and Sustainable Cities and Communities.

- The scope of the SSCC-CG is to advise on European interests and needs relating to standardization on Smart and Sustainable cities and communities.

● Status:

- Originally conceived to be completed by the end of 2014, SSCC-CG's mandate has been extended by the European standards organizations CEN, CENELEC and ETSI by a further two years and will run until the end of 2016.^{^[42]}

- The SSCC-CG does not develop standards, but reports directly to the management boards of the standardization organizations and plays an advisory role. Current members of the SSCC.CG include representatives of the relevant technical committees, the CEN/CENELEC secretariat, the European Commission, the European associations and the national standardization organizations.^{^[43]}

- CEN/CENELEC/ETSI Joint Working Group on Standards for Smart Grids: The aim of this document is to provide a strategic report which outlines the standardization requirements for implementing the European vision of smart grids, especially taking into account the initiatives by the Smart Grids Task Force of the European Commission. It provides an overview of standards, current activities, fields of action, international cooperation and strategic recommendations^{^[44]}

12. Singapore

● Background:

- In the year 2015, SPRING Singapore, the Infocomm Development Authority of Singapore (IDA) and the Information Technology Standards Committee (ITSC), under the purview of the Singapore Standards Council (SSC), have laid out an Internet of Things (IoT) Standards Outline in support of Singapore's Smart Nation initiative.

● Objective:

- Realising importance of standards in laying the foundation for the nation empowered by big data, analytics technology and sensor networks in light of Singapore's vision of becoming a Smart Nation.

● Status:

Three types of standards - sensor network standards, IoT foundational standards and domain-specific standards - have been identified under the IoT Standards Outline. Singapore actively participates in the ISO Technical Committee (TC) working on smart city standards.^{^[45]}

^{^[1]} ISO/IEC JTC 1, Information Technology, http://www.iso.org/iso/jtc1_home.html

^{^[2]} The InterNational Committee for Information Technology Standards, JTC 1 Working Group on Big Data, http://www.incits.org/committees/big-data

^{^[3]} ISO/IEC JTC 1 Forms Two Working Groups on Big Data and Internet of Things, 27th January 2015, https://www.ansi.org/news_publications/news_story.aspx?menuid=7&articleid=5b101d27-47b5-4540-bca3-657314402591

^{^[4]} JTC 1 November 2014 Resolution 28 - Establishment of a Working Group on Big Data, and Call for Participation, 20th January 2015, http://jtc1sc32.org/doc/N2601-2650/32N2625-J1N12445_JTC1_Big_Data-call_for_participation.pdf

^{^[5]} SD-3: Study Group Organizational Information, https://isocpp.org/std/standing-documents/sd-3-study-group-organizational-information

^{^[6]} ISO/IEC JTC 1 Study Group on Big Data (BD-SG), http://jtc1bigdatasg.nist.gov/home.php

^{^[7]} NIST Released V1.0 Seven Volumes of Big Data Interoperability Framework (September 16, 2015),http://bigdatawg.nist.gov/home.php

^{^[8]} Standards That Support Big Data, Monica Rozenfeld, 8th September 2014, http://theinstitute.ieee.org/benefits/standards/standards-that-support-big-data

^{^[9]} ITU releases first ever big data standards, Madolyn Smith, 21st December 2015, http://datadrivenjournalism.net/news_and_analysis/itu_releases_first_ever_big_data_standards#sthash.m3FBt63D.dpuf

^{^[10]} ITU-T Y.3600 (11/2015) Big data - Cloud computing based requirements and capabilities, http://www.itu.int/itu-t/recommendations/rec.aspx?rec=12584

^{^[11]} ISO Strategic Advisory Group on Smart Cities - Demand-side survey, March 2015, http://www.platform31.nl/uploads/media_item/media_item/41/62/Toelichting_ISO_Smart_cities_Survey-1429540845.pdf

^{^[12]} The German Standardization Roadmap Smart City Version 1.1, May 2015, https://www.vde.com/en/dke/std/documents/nr_smartcity_en_v1.1.pdf

^{^[13]} ISO/TR 37150:2014 Smart community infrastructures -- Review of existing activities relevant to metrics, http://www.iso.org/iso/catalogue_detail?csnumber=62564

^{^[14]} Dissecting ISO 37120: Why this new smart city standard is good news for cities, 30th July 2014, http://smartcitiescouncil.com/article/dissecting-iso-37120-why-new-smart-city-standard-good-news-cities

^{^[15]} World Council for City Data, http://www.dataforcities.org/wccd/

^{^[16]} Global City Indicators Facility, http://www.cityindicators.org/

^{^[17]} How to measure the performance of smart cities, Maria Lazarte, 5th October 2015

http://www.iso.org/iso/home/news_index/news_archive/news.htm?refid=Ref2001

^{^[18]} http://iet.jrc.ec.europa.eu/energyefficiency/sites/energyefficiency/files/files/documents/events/slideslairoctober2014.pdf

^{^[19]} A standard for improving communities reaches final stage, Clare Naden, 12th February 2015,

http://www.iso.org/iso/news.htm?refid=Ref1932

^{^[20]} http://iet.jrc.ec.europa.eu/energyefficiency/sites/energyefficiency/files/files/documents/events/slideslairoctober2014.pdf

^{^[21]} ISO/TR 12859:2009 Intelligent transport systems -- System architecture -- Privacy aspects in ITS standards and systems, http://www.iso.org/iso/catalogue_detail.htm?csnumber=52052

^{^[22]} ISO/IEC JTC 1 Information technology, WG 11 Smart Cities, http://www.iec.ch/dyn/www/f?p=103:14:0::::FSP_ORG_ID,FSP_LANG_ID:12973,25

^{^[23]} Work of ISO/IEC JTC1 Smart Ci4es Study group , https://interact.innovateuk.org/documents/3158891/17680585/2+JTC1+Smart+Cities+Group/e639c7f6-4354-4184-99bf-31abc87b5760

^{^[24]} JTC1 SAC - Meeting 13 , February 2015, http://www.finance.gov.au/blog/2015/08/05/jtc1-sac-meeting-13-february-2015/

^{^[25]} The German Standardization Roadmap Smart City Version 1.1, May 2015, https://www.vde.com/en/dke/std/documents/nr_smartcity_en_v1.1.pdf

^{^[26]} The German Standardization Roadmap Smart City Version 1.1, May 2015, https://www.vde.com/en/dke/std/documents/nr_smartcity_en_v1.1.pdf

^{^[27]} ITU standards to integrate Internet of Things in Smart Cities, 10th June 2015, https://www.itu.int/net/pressoffice/press_releases/2015/22.aspx

^{^[28]} ITU-T Focus Group Smart Sustainable Cities, https://www.itu.int/dms_pub/itu-t/oth/0b/04/T0B0400004F2C01PDFE.pdf

^{^[29]} Focus Group on Smart Sustainable Cities, http://www.itu.int/en/ITU-T/focusgroups/ssc/Pages/default.aspx

^{^[30]} The German Standardization Roadmap Smart City Version 1.1, May 2015, https://www.vde.com/en/dke/std/documents/nr_smartcity_en_v1.1.pdf

^{^[31]} 7001 - PRIPARE Smart City Strategy, https://eu-smartcities.eu/commitment/7001

^{^[32]} Financing Tomorrow's Cities: How Standards Can Support the Development of Smart Cities, http://www.longfinance.net/groups7/viewdiscussion/72-financing-financing-tomorrow-s-cities-how-standards-can-support-the-development-of-smart-cities.html?groupid=3

^{^[33]} BSI-Smart Cities, http://www.bsigroup.com/en-GB/smart-cities/

^{^[34]} New Set of Smart Cities Standards in Spain, https://eu-smartcities.eu/content/new-set-smart-cities-standards-spain

^{^[35]} Technical Report, M2M & ICT Enablement in Smart Cities, Telecommunication Engineering Centre, Department of Telecommunications, Ministry of Communications and Information Technology, Government of India, November 2015, http://tec.gov.in/pdf/M2M/ICT%20deployment%20and%20strategies%20for%20%20Smart%20Cities.pdf

^{^[36]} Smart City Development in China, Don Johnson, 17th June 2014, http://www.chinabusinessreview.com/smart-city-development-in-china/

^{^[37]} China to continue develop standards on smart cities, 17th December 2015, http://www.chinadaily.com.cn/world/2015wic/2015-12/17/content_22732897.htm

^{^[38]} The German Standardization Roadmap Smart City, April 2014, https://www.dke.de/de/std/documents/nr_smart%20city_en_version%201.0.pdf

^{^[39]} This version of the Smart City Standardization Roadmap, Version 1.1, is an incremental revision of Version 1.0. In Version 1.1, a special focus is placed on giving an overview of current standardization activities and interim results, thus illustrating German ambitions in this area.

^{^[40]} SSCC-CG Final report Smart and Sustainable Cities and Communities Coordination Group, January 2015, https://www.etsi.org/images/files/SSCC-CG_Final_Report-recommendations_Jan_2015.pdf

^{^[41]} Orchestrating infrastructure for sustainable Smart Cities , http://www.iec.ch/whitepaper/pdf/iecWP-smartcities-LR-en.pdf

^{^[42]} Urbanization- Why do we need standardization?, http://www.din.de/en/innovation-and-research/smart-cities-en

^{^[43]} CEN-CENELEC-ETSI Coordination Group 'Smart and Sustainable Cities and Communities' (SSCC-CG), http://www.cencenelec.eu/standards/Sectors/SmartLiving/smartcities/Pages/SSCC-CG.aspx

^{^[44]} Final report of the CEN/CENELEC/ETSI Joint Working Group on Standards for Smart Grids, https://www.etsi.org/WebSite/document/Report_CENCLCETSI_Standards_Smart%20Grids.pdf

^{^[45]} SPRING Singapore Supported Close to 600 Companies in Standards Adoption, and Service Excellence Projects , 12th August 2015, http://www.spring.gov.sg/NewsEvents/PR/Pages/Internet-of-Things-(IoT)-Standards-Outline-to-Support-Smart-Nation-Initiative-Unveiled-20150812.aspx

For more details visit https://cis-india.org/internet-governance/blog/database-on-big-data-and-smart-cities-international-standards

Curating Genderlog India's Twitter handle

Admin — 2019-05-14T14:40:08Z

Shweta Mohandas has been nominated to curate Genderlog's Twitter handle (@genderlogindia).

Shweta Mohandas will be tweeting about topics related to gender and data, more specifically around AI, big data, privacy and surveillance. To view the tweets, click here

For more details visit https://cis-india.org/internet-governance/news/curating-genderlog-indias-twitter-handle

Seminar on Understanding Financial Technology, Cashless India, and Forced Digitalisation (Delhi, January 24)

sumandro — 2017-01-23T13:17:19Z

The Centre for Financial Accountability is organising a seminar on "Understanding Financial Technology, Cashless India, and Forced Digitalisation" on Tuesday, January 24, at YWCA, Ashoka Road, New Delhi. Sumandro Chattapadhyay will participate in the seminar and speak on the emerging architecture of FinTech in India, as being developed and deployed by UIDAI and NPCI.

Cross-posted from Centre for Financial Accountability.

Programme Schedule

09.30 - Registration

10:00 - Introduction to the Seminar & Setting the Context

Madhuresh Kumar, National Alliance of People’s Movements

10:15–11:30 - Session 1 - Understanding the Political Context of FinTech

B P Mathur, Former Dy CAG

Prabir Purkayastha, Free Software Movement of India and Knowledge Commons

C P Chandrasekhar, Centre for Economic Studies and Planning, JNU

11:30-11:45 – Tea / Coffee break

11:45-13:15 - Session 2 - How will FinTech Impact the Poor, and Labour and Banking Sector?

Ashim Roy, New Trade Union of India

Nikhil Dey, Mazdoor Kisan Shakti Sangathan

Ravinder Gupta, General Secretary, State Bank of India Officers Association

13:15-14:00 – Lunch

14:00-15:30 - Session 3 - Understanding the Economic Context of FinTech

Indira Rajaraman, Former Director, RBI

Tony Joseph, Sr. Journalist

15:30-17:00 - Session 4 - Understanding the Architecture of FinTech: Linkages to Aadhaar, IndiaStack etc

Sumandro Chattapadhyay, the Centre for Internet and Society

Gopal Krishna, ToxicsWatch

17:00 – Tea

For more details visit https://cis-india.org/internet-governance/news/seminar-on-understanding-financial-technology-cashless-india-and-forced-digitalisation-delhi-jan-24-2017

Vidhi Doshi - Fingerprint Payments Prompt Privacy Fears in India (The Guardian)

Vidhi Doshi — 2017-02-13T09:21:42Z

This article by Vidhi Doshi on the use of Aadhaar-based payments by private companies in India was published by The Guardian on February 09, 2017. Sumandro Chattapadhyay is quoted in the article.

Originally published by The Guardian.

For two years, Indian officials have been trawling the country, from city slums to unelectrified villages, zapping eyeballs, scanning fingerprints and taking photographs.

Last month, Indian shoppers started to see the results. With the launch of a government-backed fingerprint payment system, tied to India’s growing biometric data bank, registered citizens can – in theory at least – now pay for things with the touch of a finger.

India’s extraordinary biometric database, named Aadhaar after a Hindi word for ‘foundation’, is the biggest of its kind in the world. It was initially sold to the public as a welfare delivery mechanism that would ensure the country’s 1.25bn citizens were each receiving the right quantity of subsidised rice or cooking fuel, while weeding out fraudsters.

But now this pool of more than a billion people’s biometric data is being used by banks, credit checking firms and other private companies to identify customers, raising questions about privacy and security.

As one of his flagship policies, prime minister Narendra Modi pledged to create a “digital India” in which the country’s cash-centric economy would switch to credit and debit cards, squeezing the parallel economy of untaxed cash transactions and giving more citizens access to digital financial services.

In a surprise television announcement last November, Modi announced the demonetisation of 500 and 1,000 rupee notes (around £6 and £12), wiping out 85% of the country’s circulating currency overnight.

Two days later, when the banks reopened, long queues snaked around almost every branch, with millions lining up to open bank accounts for the first time. Many used their 12-digit Aadhaar number, linked to their biometric profile, to sign up. Within three weeks, 3m bank accounts had been opened using fingerprint verification, according to estimates.

The moment marked a radical change for India’s banking system, under which applicants were traditionally required to file photocopies of passports or voter IDs. Banks could take weeks, sometimes months, to verify them. Now applicants’ encrypted biometric data can be sent to the Unique Identification Authority of India (UIDAI), a government agency, to be matched against their Aadhaar data, re-encrypted and sent back to the bank.

Despite technical teething problems, the system is designed to allow very fast authorisation. “All this happens in a matter or two or three seconds,” explains Ajay Bhushan Pandey, UIDAI’s director general.

For Pandey, the benefits are clear: paper documents are easy to forge and hard to verify, especially in India where until recently thousands of people still used handwritten passports. Not so biometric data.

Privacy fears

Pandey emphasises that private banks and companies aren’t able to access the entire Aadhaar database, only to use the government interface, which allows them to verify identities.

Nonetheless, many Indians are worried about the privacy implications. Sumandro Chattapadhyay, a director at the Centre for Internet and Society thinktank, is one of them.

For starters, says Chattapadhyay, the law governing use of the biometric database, fast-tracked through parliament last year, is flimsy when it comes to the private sector. Since India lacks a general privacy or data protection law, this leaves corporate use of Aadhaar services effectively unregulated, he says.

This is particularly worrying, says Chattapadhyay, because of the data-sharing possibilities opened up by Aadhaar. It makes it easier for companies not only to share information on individuals’ consumption and mobility habits, but also to link this data up with public records like the electoral register, he says. “Both lead to significant threats to privacy of individuals.”

Chattapadhyay’s fear is that private companies could eventually gain access to government-held personal data, such as income or medical records, while the government could use company data like phone records to target specific individuals in political campaigns.

Already companies are linking Aadhaar numbers with collected metadata. Credit-checking startup CreditVidya, for example, identifies clients using their biometric ID in combination with their internet browsing history and other data, to assign credit scores for users who have no record of loan repayments. Banks then store this processed metadata, for example whether or not someone’s Facebook name is consistent with the name on their bank account.

Its founder Abhishek Agarwal admits there are risks for users: “[I]f someone managed to hack the bank’s security system, as well as the Aadhaar database, they could potentially be able to link your Facebook or LinkedIn data with your biometric information.” But he says this would be hard to do.

Pandey insists the companies are carefully vetted before they can use Aadhaar authentication. But, like Agarwal, he acknowledges the system can never be 100% secure: ““I wouldn’t say it is impossible to break the system, but it is very, very difficult.”

For more details visit https://cis-india.org/internet-governance/news/vidhi-doshi-fingerprint-payments-prompt-privacy-fears-in-india-the-guardian

The Technology behind Big Data

Geethanjali Jujjavarapu and Udbhav Tiwari — 2016-12-04T09:53:43Z

The authors undertakes a high-level literature review of the most commonly used technological tools and processes in the big data life cycle. The big data life cycle is a conceptual construct that can be used to study the various stages that typically occur in collecting, storing and analysing big data, along with the principles that can govern these processes.

Download the Paper (PDF, 277 kb)

Introduction

Defining big data is a disputed area in the field of computer science^{^[1]}, there is some consensus on a basic structure to its definition^{^[2]}. Big data is data that is collected in the form of datasets that has three main criteria: size, variety & velocity, all of which operate at an immense scale^{^[3]}. It is ‘big’ in size, often running into petabytes of information, has vast variety within its components, and is created, captured and analysed at an incredibly rapid velocity. All of this also makes big data difficult to handle using traditional technological tools and techniques.

This paper will attempt to perform a high-level literature review of the most commonly used technological tools and processes in the big data life cycle. The big data life cycle is a conceptual construct that can be used to study the various stages that typically occur in collecting, storing and analysing big data, along with the principles that can govern these processes. The big data life cycle consists of four components, which will also be the key structural points of the paper, namely: Data Acquisition, Data Awareness, Data Analytics & Data Governance.⁴ The paper will focus on the aspects that the author believes are relevant for analysing the technological impact of big data on both technology itself and society at large.

Scope: The scope of the paper is to study the technology used in big data using the "Life Cycle of Big Data" as model structure to categorise & study the vast range of technologies that are involved in big data. However, the paper will be limited to the study of technology related directly to the big data life cycle. It shall specifically exclude the use/utilisation of big data from its scope since big data is most often being fed into other, unrelated technologies for consumption leading to rather limitless possibilities.

Goal: Goal of the paper is twofold: a.) to use the available literature on the technological aspects of big data, to perform a brief overview of the technology in the field and b.) to frame the relevant research questions for studying the technology of big data and its possible impact on society.

Data Acquisition

Acquiring big data has two main sub components to it, the first being sensing the existence of the data’ itself and the second, the stage of collecting and storing this data. Both of these subcomponents are incredibly diverse fields, with lots of rapid change occurring in the technology utilised to carry out these tasks. The section will provide a brief overview of the subcomponents and then discuss the technology used to fulfil the tasks.

Data Sensing

Data does not exist in a vacuum and is always created as a part of a larger process, especially in the aspect of modern technology. Therefore, the source of the data itself plays a vital role in determining how it can be captured and analysed in the larger scheme of things. Entities constantly emit information into the environment that can be utilised for the purposes of big data, leading to two main kinds of data: data that is “born digital” or “born analogue.”^{^[4]}

Born Digital Data

Information that is “born digital,” is created, by a user or by a digital system, specifically for use by a computer or data‐processing system. This is a vast range of information and newer fields are being added to this category on a daily basis. It includes, as a short, indicative list: email and text messaging, any form of digital input, including keyboards, mouse interactions and touch screens, GPS location data, data from daily home appliances (Internet of Things), etc. All of this data can be tracked and tagged to users as well as be aggregated to form a larger picture, massively increasing the scope of what may constitute the ‘data’ in big data.

Some indicative uses of how such born digital data is catalogued by technological solutions on the user side, prior to being sent for collection/storage are:

a.) Cookies - There are small, often just text, files that are left on user devices by websites in order to that visit, task or action (for example, logging into an email account) with a subsequent event.^{^[5]} (for example, revisiting the website)

b.) Website Analytics^{^[6]} - Various services, such as Google Analytics, Piwik, etc., can use JavaScript and other web development languages to record a very detailed, intimate track of a user's actions on a website, including how long a user hovers above a link, the time spent on the website/application and in some cases, even the time spent specific aspects of the page.

c.) GPS^{^[7]} - With the almost pervasive usage of smartphones with basic location capabilities, GPS sensors on these devices are used to provide regular, minute driven updates to applications, operating systems and even third parties about the user's location. Modern variations such as A-GPS can be used to provide basic positioning information even without satellite coverage, vastly expanding the indoor capabilities of location collection.

All of these instances of sensing born digital data are common terms, used in daily parlance by billions of people from all over the world, which is a symbolic of just how deeply they have pervaded into our daily lifestyle. Apart from privacy & security concerns this in turn also leads to an exponential increase in the data available to collect for any interested party.

Sensor Data

Information is said to be “analogue” when it contains characteristics of the physical world, such as images, video, heartbeats, etc. Such information becomes electronic when processed by a “sensor,” a device that can record physical phenomena and convert it into digital information. Some examples to better illustrate information that is born analogue but collected via digital means are:

a.) Voice and/or video content on devices - Apart from phone calls and other forms communication, video and voice based interactions have started to regularly be captured to provide enhanced services. These include Google Now^{^[8]}, Cortana^{^[9]} and other digital assistants as well as voice guided navigation systems in cars, etc.

b.) Personal health data such as heartbeats, blood pressure, respiration, velocity, etc. - This personal, potentially very powerful information is collected by dedicated sensors on devices such as Fitbit^{^[10]}, Mi Band^{^[11]}, etc. as well as by increasingly sophisticated smartphone applications such as Google Fit that can do so without any special device.

c.) Camera on Home Appliances - Cameras and sensors on devices such as video game consoles (Kinect^{^[12]} being a relevant example) can record detailed human interactions, which can be mined for vast amounts of information apart from carrying out the basic interactions with the devices itself.

While not as vast a category as born digital data, the increasingly lower costs of technology and ubiquitous usage of digital, networked devices is leading to information that was traditionally analogue in nature to be captured for use at a rapidly increasing rate.

Data Collection & Storage

Traditional data was normally processed using the Extract, Transform, Load (ETL) methodology, which was used to collect the data from outside sources, modify the data to fit needs, and then upload the data into the data storage system for future use.^{^[13]} Technology such as spreadsheets, RDBMS databases, Structured Query Languages (SQL), etc. were all initially used to carry out these tasks, more often than not manually. ^{^[14]}

However, for big data, the methodology traditionally followed is both inefficient and insufficient to meet the demands of modern use. Therefore, the Magnetic, Agile, Deep (MAD) process is used to collect and store data^{^[15]}^{^[16]}. The needs and benefits of such a system are: attracting all the data sources regardless of their quality (magnetic), logical and physical contents of storage systems adapting to the rapid data evolution in big data (agile) and complex algorithmic statistical analysis required of big data on a very short notice^{^[17]}. (deep)

The technology used to perform data storage using the MAD process requires vast amount of processing power, which is very difficult to create in a single, physical space/unit for nonstate or research entities, who cannot afford supercomputers. Therefore, most solutions used in big data rely on two major components to store data: distributed systems and Massive Parallel Processing^{^[18]} (MPP) that run on non-relational (in-memory) database systems. Database performance and reliability is traditionally gauged using pure performance metrics (FLOPS per second, etc.) as well as the Atomicity, consistency, isolation, durability (ACID) criteria.^{^[19]} The most commonly used database systems for big data applications are given below. The specific operational qualities and performance of each of these databases is beyond the scope of this review but the common criteria that makes them well suited for big data storage have been delineated below.

Non-relational databases

Databases traditionally used to be structured entities that operated solely on the ability to correlate information stored in them using explicitly defined relationships. Even prior to the advent of big data, this outlook was turning out to be a limiting factor in how large amounts of stored information could be leveraged, this led to the evolution of non relational database systems. Before going into them in detail, a basic primer on their data transfer protocols will be helpful in understanding their operation.

A protocol is a model that structures instructions in a particular manner so that it can be reproduced from one system to another^{^[20]}^{^[21]}. The protocols which govern technology in the case of big data have gone through many stages of evolution, starting off with simple HTML based systems^{^[22]}, which then evolved to XML driven SOAP systems^{^[23]}, which led to JavaScript Object Notation, or JSON^{^[24]}, the currently used form for in most big database systems. JSON is an open format used to transfer data objects, using human-readable text and is the basis for most of the commonly used non-relational database management systems. Examples of Non-relational databases also known as NoSQL databases, include MongoDB^{^[25]}, Couchbase^{^[26]}, etc. They were developed for both managing as well as storing unstructured data. They aim for scaling, flexibility, and simplified development. Such databases rather focus on the high-performance scalable data storage, and allow tasks to be written in the application layer instead of databases specific languages, allowing for greater interoperability.^{^[27]}

In-Memory Databases

In order to overcome performance limitation of traditional database systems, some modern databases now use in-memory databases. These systems manage the data in the RAM memory of the server, thus eliminating storage disk input/output. This allows for almost realtime responses from the database, in comparisons to minutes or hours required on traditional database systems. This improvement in the performance is so massive that, entirely new applications are being developed for using IMDB systems.^{^[28]} These IMDB systems are also being used for advanced analytics on big data, especially to increase the access speed to data and increase the scoring rate of analytic models for analysis.^{^[29]} Examples of IMDB include VoltDB^{^[30]}, NuoDB^{^[31]}, SolidDB^{^[32]} and Apache Spark^{^[33]}.

Hybrid Systems

These are the two major systems used to store data prior to it being processed or analysed in a big data application. However, the divide between data storage and data management is a slim one and most database systems also contain various unique attributes that cater them to specific kinds of analysis. (as can be seen from the IMDB example above) One example of a very commonly used Hybrid system that deals with storage as well as awareness of the data is Apache Hadoop³³, which is detailed below.

Apache Hadoop

Hadoop consists of two main components: the HDFS for the big data storage, and MapReduce for big data analytics, each of which will be detailed in their respective section.

The HDFS^{^[34]}^{^[35]} storage function in Hadoop provides a reliable distributed file system, stored across multiple systems for processing & redundancy reasons. The file system is optimized for large files, as single files are split into blocks and spread across systems known as cluster nodes.^{^[36]} Additionally, the data is protected among the nodes by a replication mechanism, which ensures availability even if any node fails. Further, there are two types of nodes: Data Nodes and Name Nodes.^{^[37]} Data is stored in the form of file blocks across the multiple Data Nodes while the Name Node acts as an intermediary between the client and the Data Node, where it directs the requesting client to the particular Data Node which contains the requested data.

This operating structure for storing data also has various variations within Hadoop such as HBase for key/value pair type queries (a NoSQL based system), Hive for relational type queries, etc. Hadoop’s redundancy, speed, ability to run on commodity hardware, industry support and rapid pace of development have led to it being almost co-equivalently associated with big data.^{^[38]}

Data Awareness

Data Awareness, in the context of big data, is the task of creating a scheme of relationships within a set of data, to allow different users of the data to determine a fluid yet valid context and utilise it for their desired tasks.^{^[39]} It is a relatively new field, in which most of the work is currently being done on semantic structures to allow data to gain context in an interoperable format, in contrast to the current system where data is given context using unique, model specific constructs.^{^[40]} (such as XML Schemes, etc.)

Some of the original work on this field was carried out in the form of utilising the Resource Description Framework (RDF), which was built primarily to allow describing of data in a portable manner, especially being agnostic towards platforms and systems for Semantic Web at the W3C. SPARQL is the language used to implement RDF based designs but both largely remain underutilised in both the public domain as well as big data. Authors such as Kurt

Cagle^{^[41]} and Bob DuCharme^{^[42]} predict its explosion in the next couple of years. Companies have also started realising the value of interoperable context, with Oracle Spatial^{^[43]} and IBM’s DB2^{^[44]} already including RDF and SPARQL support in the past 3 years.

While underutilised, the rapid developments taking place in the field will make the impact that data awareness may have on big data as big as Hadoop and maybe even SQL. Some aspects of it are already beginning to be used in Artificial Intelligence, Natural Language Processing, etc. with tremendous scope for development.^{^[45]}

Data Processing & Analytics

Data Processing largely has three primary goals: a. determines if the data collected is internally consistent; b. make the data meaningful to other systems or users using either metaphors or analogy they can understand; and (what many consider most importantly) provide predictions about future events and behaviours based upon past data and trends.^{^[46]}

Being a very vast field with rapidly changing technologies governing its operation, this section will largely concentrate on the most commonly used technologies in data analytics.

Data analytics requires four primary conditions to be met in order to carry out effective processing: fast, data loading, fast query processing, efficient utilisation of storage and adaptivity to dynamic workload patterns. The analytical model most commonly associated with meeting this criteria and with big data in general is MapReduce, detailed below. There are other, more niche models and algorithms (such as Project Voldemort^{^[47]} used by LinkedIn), which are used in big data but they are beyond the scope of the review, and more information about them can be read at article linked in the previous citation. (Reference architecture and classification of technologies, products and services for big data system)

MapReduce

MapReduce is a generic parallel programming concept, derived from the “Map” and “Reduce” of functional programming languages, which makes it particularly suited for big data operations. It is at the core of Hadoop^{^[48]}, and performs the data processing and analytics functions in other big data systems as well.^{^[49]} The fundamental premise of MapReduce is scaling out rather than scaling up, i.e., (adding more numerical resources, rather than increasing the power of a single system)^{^[50]}

MapReduce operates by breaking a task down into steps and executing the steps in parallel, across many systems. This comes with two advantages, a reduction in the time needed to finish the task and also a decrease in the amount of resources one has to expend to perform the task, in both power and energy. This model makes it ideally suited for the large data sets and quick response times required of big data operations generally.

The first step of a MapReduce job is to correlate the input values to a set of keys/value pairs as output. The “Map” function then partitions the processing tasks into smaller tasks, and assigns them to the appropriate key/value pairs.^{^[51]} This allows unstructured data, such as plain text, to be mapped to a structured key/value pair. As an example, the key could be the punctuation in a sentence and the value of the pair could be the number of occurrences of the punctuation overall. This output of the Map function is then passed on “Reduce” function.^{^[52]} Reduce then collects and combines this output, using identical key/value pairs, to provide the final result of the task.^{^[53]} These steps are carried using the Job Tracker & Task Tracker in Hadoop but different systems have different methodologies to carry out similar tasks.

Data Governance

Data Governance is the act of managing raw big data as well as the processed information that arises from big data in order to meet legal, regulatory and business imposed requirements. While there is no standardized format for data governance, there have been increasing call with various sectors (especially healthcare) to create such a format to ensure reliable, secure and consistent big data utilisation across the board. The following tactics and techniques have been utilised or suggested for data governance, with varying degrees of success:

Zero-knowledge systems: This technological proposal maintains secrecy with respect to the low-level data while allowing encrypted data to be examined for certain higherlevel abstractions.^{^[54]} For the system to be zero-knowledge, the client’s system will have to encrypt the data and send it to the storage provider. Due to this, the provider stores the data in the encrypted format and cannot decipher the same unless he/she is in possession of the key which will decrypt the data into plaintext. This allows the individual to store his data with a storage provider while also maintaining anonymity of the details contained in such information. However, these are currently just beginning to be used in simple situations. As of now, they are not expandable to unstructured and complex cases and have to be developed marginally before they can be used for research and data mining purposes.
Homomorphic encryption: Homomorphic encryption is a privacy preserving technique which performs searches and other computations over data that is encrypted while also protecting the individual’s privacy.^{^[55]} This technique has however been considered to be impractical and is deemed to be an unlikely policy alternative for near future purposes in the context of preserving privacy in the age of big data.^{^[56]}
Multi-party computation: In this technique, computation is done on encrypted distributed data stores.^{^[57]} This mechanism is closely related to homomorphic encryption where individual data is kept private using encryption algorithms called “collusion-robust” while the same is used to calculate statistics.^{^[58]} The parties involved are aware of some private data and each of them use a protocol which produces results based on the information they are aware of and the information they are not aware of, without revealing the data they are not already aware of.^{^[59]} Multi-party computations thus help in generating useful data for statistical and research purposes without compromising the privacy of the individuals.

Differential Privacy: Although this technological development is related to encryption, it follows a different technique. Differential privacy aims at maximizing the precision of computations and database queries while reducing the identifiability of the data owners who have records in the database, usually through obfuscation of query results.^{^[60]} This is widely applied today in the existence of big data in order to ensure preservation of privacy while trying to reap the benefits of large scale data collection.^{^[61]}
Searchable encryption: Through this mechanism, the data subject can make certain data searchable while minimizing exposure and maximizing privacy.^{^[62]} The data owner can make his information available through search engines by providing the data in an encrypted format but by adding tags consisting of certain keywords which can be deciphered by the search engine. This encrypted data shows up in the search results when searched with these particular keywords but can only be read when the person is in possession of the key which is required for decrypting the information.

This technique of encryption provides maximum security to the individual’s data and preserves privacy to the greatest possible extent.

K-anonymity: The property of k-anonymity is being applied in the present day in order to preserve privacy and avoid re-identification.^{^[63]} A certain data set is said to possess the property of k-anonymity if individual specific data can be released and used for various purposes without re-identification. The analysis of the data should be carried out without attributing the data to the individual to whom it belongs and should give scientific guarantees for the same.
Identity Management Systems: These systems enable the individuals to establish and safeguard their identities, explain those identities with the help of attributes, follow the activity of their identities and also delete their identities if they wish to.^{^[64]} It uses cryptographic schemes and protocols to make anonymous or pseudonymous the identities and credentials of the individuals before analysing the data.
Privacy Preserving Data Publishing: This is a method in which the analysts are provided with the individual’s personal information with the ability to decipher particular information from the database while preventing the inference of certain other information which might lead to a breach of privacy.^{^[65]} Data which is essential for the analysis will be provided for processing while sensitive data will not be disclosed. This tool primarily focuses on microdata.
Privacy Preserving Data Mining: This mechanism uses perturbation methods and randomization along with cryptography in order to permit data mining on a filtered version of the data which does not contain any form of sensitive information. PPDM focuses on data mining results unlike PPDP.^{^[66]}

Conclusion

Studying the technology surrounding big data has led to two major observations: the rapid pace of development in the industry and the stark lack of industry standards or government regulations directed towards big data technologies. These observations have been the primary motivating factor for framing further research in the field. Understanding how to deal with big data technologically, rather than just the potential regulation of possible harms after the technological processes have been performed might be critical for the human rights dialogue as these processes become even more extensive, opaque and technologically complicated.

[1] EMC: Data Science and Big Data Analytics. In: EMC Education Services, pp. 1–508 (2012)

[2] Bakshi, K.: Considerations for Big Data: Architecture and Approaches. In: Proceedings of the IEEE Aerospace Conference, pp. 1–7 (2012)

[3] Adams, M.N.: Perspectives on Data Mining. International Journal of Market Research 52(1), 11–19 (2010) ⁴ Elgendy, N.: Big Data Analytics in Support of the Decision Making Process. MSc Thesis, German University in Cairo, p. 164 (2013)

[4] Big Data and Privacy: A Technological Perspective - President’s Council of Advisors on Science and

Technology (May 2014)

[5] Chen, Hsinchun, Roger HL Chiang, and Veda C. Storey. "Business Intelligence and Analytics: From Big Data to Big Impact." MIS quarterly 36.4 (2012): 1165-1188.

[6] Chandramouli, Badrish, Jonathan Goldstein, and Songyun Duan. "Temporal analytics on big data for web advertising." 2012 IEEE 28th international conference on data engineering. IEEE, 2012.

[7] Laurila, Juha K., et al. "The mobile data challenge: Big data for mobile computing research." Pervasive Computing. No. EPFL-CONF-192489. 2012.

[8] Lazer, David, et al. "The parable of Google flu: traps in big data analysis." Science 343.6176 (2014): 12031205.

[9] ibid

[10] Banaee, Hadi, Mobyen Uddin Ahmed, and Amy Loutfi. "Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges." Sensors 13.12 (2013): 17472-17500.

[11] ibid

[12] Chung, Eric S., John D. Davis, and Jaewon Lee. "Linqits: Big data on little clients." ACM SIGARCH Computer Architecture News. Vol. 41. No. 3. ACM, 2013.

[13] Kornelson, Kevin Paul, et al. "Method and system for developing extract transform load systems for data warehouses." U.S. Patent No. 7,139,779. 21 Nov. 2006.

[14] Henry, Scott, et al. "Engineering trade study: extract, transform, load tools for data migration." 2005 IEEE Design Symposium, Systems and Information Engineering. IEEE, 2005.

[15] Cohen, Jeffrey, et al. "MAD skills: new analysis practices for big data." Proceedings of the VLDB Endowment

[16] .2 (2009): 1481-1492.

[17] Elgendy, Nada, and Ahmed Elragal. "Big data analytics: a literature review paper." Industrial Conference on Data Mining. Springer International Publishing, 2014.

[18] Wu, Xindong, et al. "Data mining with big data." IEEE transactions on knowledge and data engineering 26.1 (2014): 97-107.

[19] Supra Note 17

[20] Hu, Han, et al. "Toward scalable systems for big data analytics: A technology tutorial." IEEE Access 2 (2014):

[21] -687.

[22] Kurt Cagle, Understanding the Big Data Lifecycle - LinkedIn Pulse (2015)

[23] Coyle, Frank P. XML, Web services, and the data revolution. Addison-Wesley Longman Publishing Co., Inc., 2002.

[24] Pautasso, Cesare, Olaf Zimmermann, and Frank Leymann. "Restful web services vs. big'web services: making the right architectural decision." Proceedings of the 17th international conference on World Wide Web. ACM, 2008.

[25] Banker, Kyle. MongoDB in action. Manning Publications Co., 2011

[26] McCreary, Dan, and Ann Kelly. "Making sense of NoSQL." Shelter Island: Manning (2014): 19-20.

[27] ibid

[28] Zhang, Hao, et al. "In-memory big data management and processing: A survey." IEEE Transactions on Knowledge and Data Engineering 27.7 (2015): 1920-1948.

[29] ibid

[30] ibid

[31] Supra Note 20

[32] Ballard, Chuck, et al. IBM solidDB: Delivering Data with Extreme Speed. IBM Redbooks, 2011.

[33] Shanahan, James G., and Laing Dai. "Large scale distributed data science using apache spark." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. ³³ Shvachko, Konstantin, et al. "The hadoop distributed file system." 2010 IEEE 26th symposium on mass storage systems and technologies (MSST). IEEE, 2010.

[34] Borthakur, Dhruba. "The hadoop distributed file system: Architecture and design." Hadoop Project Website

[35] .2007 (2007): 21.

[36] ibid

[37] ibid

[38] Zikopoulos, Paul, and Chris Eaton. Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media, 2011.

[39] Bizer, Christian, et al. "The meaningful use of big data: four perspectives--four challenges." ACM SIGMOD Record 40.4 (2012): 56-60.

[40] Kaisler, Stephen, et al. "Big data: issues and challenges moving forward." System Sciences (HICSS), 2013 46th Hawaii International Conference on. IEEE, 2013.

[41] Supra Note 21

[42] DuCharme, Bob. "What Do RDF and SPARQL bring to Big Data Projects?." Big Data 1.1 (2013): 38-41.

[43] Zhong, Yunqin, et al. "Towards parallel spatial query processing for big spatial data." Parallel and

Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International. IEEE, 2012.

[44] Ma, Li, et al. "Effective and efficient semantic web data management over DB2." Proceedings of the 2008 ACM SIGMOD international conference on Management of data. ACM, 2008.

[45] Lohr, Steve. "The age of big data." New York Times 11 (2012).

[46] Pääkkönen, Pekka, and Daniel Pakkala. "Reference architecture and classification of technologies, products and services for big data systems." Big Data Research 2.4 (2015): 166-186.

[47] Sumbaly, Roshan, et al. "Serving large-scale batch computed data with project voldemort." Proceedings of the 10th USENIX conference on File and Storage Technologies. USENIX Association, 2012.

[48] Bar-Sinai, Michael. "Big Data Technology Literature Review." arXiv preprint arXiv:1506.08978 (2015).

[49] ibid

[50] Condie, Tyson, et al. "MapReduce Online." Nsdi. Vol. 10. No. 4. 2010.

[51] Supra Note 47

[52] Dean, Jeffrey, and Sanjay Ghemawat. "MapReduce: a flexible data processing tool." Communications of the ACM 53.1 (2010): 72-77.

[53] ibid

[54] Big Data and Privacy: A Technological Perspective, White House,

https://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy__may_2014

[55] Tene, Omer, and Jules Polonetsky. "Big data for all: Privacy and user control in the age of analytics." Nw. J. Tech. & Intell. Prop. 11 (2012): xxvii.

[56] Big Data and Privacy: A Technological Perspective, White House,

https://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy__may_2014

[57] Privacy by design in big data, ENISA

[58] Big Data and Privacy: A Technological Perspective, White House,

https://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy__may_2014

[59] Id

[60] Id

[61] Tene, Omer, and Jules Polonetsky. "Privacy in the age of big data: a time for big decisions." Stanford Law Review Online 64 (2012): 63.

[62] Lane, Julia, et al., eds. Privacy, big data, and the public good: Frameworks for engagement. Cambridge University Press, 2014.

[63] Crawford, Kate, and Jason Schultz. "Big data and due process: Toward a framework to redress predictive privacy harms." BCL Rev. 55 (2014): 93.

[64] http://homes.esat.kuleuven.be/~sguerses/papers/DanezisGuersesSurveillancePets2010.pdf

[65] Seda Gurses and George Danezis, A critical review of 10 years of privacy technology, August 12th 2010, http://homes.esat.kuleuven.be/~sguerses/papers/DanezisGuersesSurveillancePets2010.pdf

[66] Id

For more details visit https://cis-india.org/internet-governance/blog/technology-behind-big-data

Can data ever know who we really are?

sumandro — 2019-12-06T05:02:53Z

This is an excerpt from an essay by Zara Rahman, written for and published as part of the Bodies of Evidence collection of Deep Dives. The Bodies of Evidence collection, edited by Bishakha Datta and Richa Kaul Padte, is a collaboration between Point of View and the Centre for Internet and Society, undertaken as part of the Big Data for Development Network supported by International Development Research Centre, Canada.

Please read the full essay on Deep Dives: Can data ever know who we really are?

Zara Rahman: The Engine Room, Website, and Twitter

If I didn’t define myself for myself, I would be crunched into other people’s fantasies for me and eaten alive.
– Audre Lorde

The proliferation of digital data and the technologies that allow us to gather that data can be used in another way too — to allow us to define for ourselves who we are, and what we are.

Amidst a growing political climate of fear, mistrust and competition for resources, activists and advocates working in areas that are stigmatised within their societies often need data to ‘prove’ that what they are working on matters. One way of doing this is by gathering data through crowdsourcing. Crowdsourced data isn’t ‘representative’, as statisticians say, but gathering data through unofficial means can be a valuable asset for advocates. For example, data collating the experiences of women who have reported incidents of sexual violence to the police in India, can then be used to advocate for better police responses, and to inform women of their rights. Deservedly or not, quantifiable data takes precedence over personal histories and lived experience in getting the much-desired currency of attention.

And used right, quantifiable data — whether it’s crowdsourced or not — can also be a powerful tool for advocates. Now, we can use quantifiable data to prove beyond a question of a doubt that disabled people, queer people, people from lower castes, face intersecting discrimination, prejudice, and systemic injustices in their lives. It’s an unnecessary repetition in a way, because anybody from those communities could have told reams upon reams of stories about discrimination — all without any need for counting.

Regardless, to play within this increasingly digitised system, we need to repeat what we’ve been saying in a new, digitally-legible way. And to do that, we need to collect data from people who have often only ever been de-humanised as data subjects.

Artist and educator Mimi Onuoha writes about the challenges that arise while collecting such data, from acknowledging the humans behind that collection to understanding that missing data points might tell just as much of a story as the data that has been collected. She outlines how digital data means that we have to (intentionally or not) make certain choices about what we value. And the collection of this data means making human choices solid, and often (though not always) making these choices illegible to others.

We speak of black boxes when it comes to the mystery choices that algorithms make, but the same could be said of the many human decisions that are made in categorising data too, whether that be choosing to limit the gender drop-down field to just ‘male/female’ as with Fitbits, or a variety of apps incorrectly assuming that all people who menstruate also want to know about their ‘fertile window’. In large systems with many humans and machines at work, we have no way of interrogating why a category was merged or not, of understanding why certain anomalies were ignored rather than incorporated, or of questioning why certain assumptions were made.

The only thing we can do is to acknowledge these limitations, and try to use those very systems to our advantage, building our own alternatives or workarounds, collecting our own data, and using the data that is out there to tell the stories that matter to us.

For more details visit https://cis-india.org/raw/zara-rahman-can-data-ever-know-who-we-really-are

CFI-ACCION - Panel Discussion on 'Big Data: Challenge or Opportunity?' (Delhi, December 06)

sumandro — 2019-03-16T04:41:52Z

The Centre for Financial Inclusion of ACCION International is organising a panel discussion on "Big Data: Challenge or Opportunity?" as an associated event of the Inclusive Finance India Summit 2016, Hotel Ashok, Delhi, December 05-06. The discussion will be held at 12:30 on Tuesday, December 06. It will be moderated by Amy Jensen Mowl, CFI Fellow at IFMR, and M.S. Sriram, Distinguished Fellow at the Institute for Development of Research in Banking Technology. Sumandro Chattapadhyay will participate as a panelist.

Inclusive Finance India Summit: http://inclusivefinanceindia.org/.

For more details visit https://cis-india.org/internet-governance/news/cfi-accion-panel-discussion-on-big-data-delhi-dec-06

Workshop Report - UIDAI and Welfare Services: Exclusion and Countermeasures

vanya — 2019-03-16T04:34:11Z

This report presents summarised notes from a workshop organised by the Centre for Internet and Society (CIS) on Saturday, August 27, 2016, to discuss, raise awareness of, and devise countermeasures to exclusion due to implementation of UID-based verification for and distribution of welfare services.

Introduction

The Centre for Internet and Society organised a workshop on "UIDAI and Welfare Services: Exclusion and Countermeasures" at the Institution of Agricultural on Technologists on August 27 in Bangalore to discuss, raise awareness of, and devise countermeasures to exclusion due to implementation of UID-based verification for and distribution of welfare services [1]. This was a follow-up to the workshop held in Delhi on “Understanding Aadhaar and its New Challenges” at the Centre for Studies in Science Policy, JNU on May 26th and 27th 2016 [2]. In this report we summarise the key concerns raised and the case studies presented by the participants at the workshop held on August 27, 2016.

Implementation of the UID Project

Question of Consent: The Aadhaar Act [3] states that the consent of the individual must be taken at the time of enrollment and authentication and it must be informed to him/her the purpose for which the data would be used. However, the Act does not provide for an opt-out mechanism and an individual is compelled to give consent to continue with the enrollment process or to complete an authentication.

Lack of Adherence to Court Orders: Despite of several orders by Supreme Court stating that use of Aadhaar cannot be made mandatory for the purpose of availing benefits and services, multiple state governments and departments have made it mandatory for a wide range of purposes like booking railway tickets [4], linking below the poverty line ration cards with Aadhaar [5], school examinations [6], food security, pension and scholarship [7], to name a few.

Misleading Advertisements: A concern was raised that individuals are being mislead in the necessity and purpose for enrollment into the project. For example, people have been asked to enrol by telling them that they might get excluded from the system and cannot get services like passports, banks, NREGA, salaries for government employees, denial of vaccinations, etc. Furthermore, the Supreme Court has ordered Aadhaar not be mandatory, yet people are being told that documentation or record keeping cannot be done without UID number.

Hybrid Governance: The participants pointed out that with the Aadhaar (Targeted delivery of financial and other subsidies, benefits and services) Act, 2016 (hereinafter referred to as Aadhaar Act, 2016 ) being partially enforced, multiple examples of exclusion as reported in the news are demonstrating how the Aadhaar project is creating a case of hybrid governance i.e private corporations playing a significant role in Governance. This can be seen in case of Aadhaar where we see many entities from private sector being involved in its implementation, as well as many software and hardware companies.

Lack of Transparency around Sharing of Biometric Data: The fact how and why the Government is relying on biometrics for welfare schemes is unclear and not known. Also, there is no information on how biometric data that is collected through the project is being used and its ability as an authenticating device. Along with that, there is very little information on companies that have been enlisted to hold and manage data and perform authentication.

Possibility of Surveillance: Multiple petitions and ongoing cases have raised concerns regarding the possibility of surveillance, tracking, profiling, convergence of data, and the opaque involvement of private companies involved in the project.

Denial of Information: In an RTI filed by one of the participant requesting to share the key contract for the project, it was refused on the grounds under section 8(1) (d) of the RTI Act, 2005. However, it was claimed that the provision would not be applicable since the contract was already awarded and any information disclosed to the Parliament should be disclosed to the citizens. The Central Information Commission issued a letter stating that the contractual obligation is over and a copy of the said agreement can be duly shared. However, it was discovered by the said participant that certain pages of the same were missing , which contained confidential information. When this issue went before appeal before the Information Commissioner, the IC gave an order to the IC in Delhi to comply with the previous order. However, it was communicated that limited financial information may be given, but not missing pages. Also, it was revealed that the UIDAI was supposed to share biometric data with NPR (by way of a MoU), but it has refused to give information since the intention was to discontinue NPR and wanted only UIDAI to collect data.

Concerns Arising from the Report of the Comptroller and Auditor General of India (CAG) on Implementation of PAHAL (DBTL) Scheme

A presentation on the CAG compliance audit report of PAHAL on LPG [8] revealed how the society was made to believe that UID will help deal with the issue of duplication and collection as well as use of biometric data will help. The report also revealed that multiple LPG connections have the same Aadhaar number or same bank account number in the consumer database maintained by the OMCs, the bank account number of consumers were also not accurately recorded, scrutiny of the database revealed improper capture of Aadhaar numbers, and there was incorrect seeding of IFSC codes in consumer database. The participants felt that this was an example of how schemes that are being introduced for social welfare do not necessarily benefit the society, and on the contrary, has led to exclusion by design. For example, in the year 2011, by was of the The Liquefied Petroleum Gas (Regulation of Supply and Distribution) Amendment Order, 2011 [9], the Ministry of Petroleum and Natural Gas made the Unique Identification Number (UID) under the Aadhaar project a must for availing LPG refills. This received a lot of public pushback, which led to non-implementation of the order. In October 2012, despite the UIDAI stating that the number was voluntary, a number of services began requiring the provision of an Aadhaar number for accessing benefits. In September 2013, when the first order on Aadhaar was passed by court [10], oil marketing companies and UIDAI approached the Supreme Court to change the same and allow them to make it mandatory, which was refused by the Court. Later in the year 2014, use of Aadhaar for subsidies was made mandatory. The participants further criticised the CAG report for revealing the manner in which linking Aadhaar with welfare schemes has allowed duplication and led to ghost beneficiaries where there is no information about who these people are who are receiving the benefits of the subsidies. For example, in Rajasthan, people are being denied their pension as they are being declared dead due to absence of information from the Aadhaar database.

It was said that the statistics of duplication mentioned in the report show how UIDAI (as it claims to ensure de-duplication of beneficiaries) is not required for this purpose and can be done without Aadhaar as well. Also, due to incorrect seeding of Aadhaar number many are being denied subsidy where there is no information regarding the number of people who have been denied the subsidy because of this. Considering these important facts from the audit report, the discussants concluded how the statistics reflect inflated claims by UIDAI and how the problems which are said to be addressed by using Aadhaar can be dealt without it. In this context, it is important to understand how the data in the aadhaar database maybe wrong and in case of e-governance the citizens suffer. Also, the fact that loss of subsidy-not in cash, but in use of LPG cylinder - only for cooking, is ignored. In addition to that, there is no data or way to check if the cylinder is being used for commercial purposes or not as RTI from oil companies says that no ghost identities have been detected.

UID-linked Welfare Delivery in Rajasthan

One speaker presented findings on people's experiences with UID-linked welfare services in Rajasthan, collected through a 100 days trip organised to speak to people across the state on problems related to welfare governance. This visit revealed that people who need the benefits and access to subsidies most are often excluded from actual services. It was highlighted that the paperless system is proving to be highly dangerous. Some of the cases discussed included that of a disabled labourer, who was asked to get an aadhaar card, but during enrollment asked the person standing next to him to put all his 5 fingers for biometric data collection. Due to this incorrect data, he is devoid of all subsidies since the authentication fails every time he goes to avail it. He stopped receiving his entitlements. Though problems were anticipated, the misery of the people revealed the extent of the problems arising from the project. In another case, an elderly woman living alone, since she could not go for Aadhaar authentication, had not been receiving the ration she is entitled to receive for the past 8 months. When the ration shop was approached to represent her case, the dealers said that they cannot provide her ration since they would require her thumb print for authentication. Later, they found out that on persuading the dealer to provide her with ration since Aadhaar is not mandatory, they found out that in their records they had actually mentioned that she was being given the ration, which was not the case. So the lack of awareness and the fact that people are entitled to receive the benefits irrespective of Aadhaar is something that is being misused by dealers. This shows how this system has become a barrier for the people, where they are also unaware about the grievance redressal mechanism.

Aadhaar and e-KYC

In this session, the use of Aadhaar for e-KYC verification was discussed The UID strategy document describes how the idea is to link UIDAI with money enabled Direct Benefit Transfer (DBT) to the beneficiaries without any reason or justification for the same. It was highlighted by one of the participants how the Reserve Bank of India (RBI) believed that making Aadhaar compulsory for e-KYC and several other banking services was a violation of the Money Laundering Act as well as its own rules and standards, however, later relaxed the rules to link Aadhaar with bank accounts and accepted its for e-KyC with great reluctance as the Department of Revenue thought otherwise. It was mentioned how allowing opening of bank accounts remotely using Aadhaar, without physically being present, was touted as a dangerous idea. However, the restrictions placed by RBI were suddenly done away with and opening bank accounts remotely was enabled via e-KYC.

A speaker emphasised that with emerging FinTech services in India being tied with Aadhaar via India Stack, the following concerns are becoming critical:

With RBI enabling creation of bank accounts remotely, it becomes difficult to to track who did e-KYC and which bank did it and hold the same accountable.
The Aadhaar Act 2016 states that UIDAI will not track the queries made and will only keep a record of Yes/No for authentication. For example, the e-KYC to open a bank account can now be done with the help of an Aadhaar number and biometric authentication. However, this request does not get recorded and at the time of authentication, an individual is simply told whether the request has been matched or not by way of a Yes/No [11]. Though UIDAI will maintain the authentication record, this may act as an obstacle since in case the information from the aadhaar database does not match, the person would not be able to open a bank account and would only receive a yes/no as a response to the request.
Further, there is a concern that the Aadhaar Enabled Payment System being implemented by the National Payment Corporation of India (NCPI) would allow effectively hiding of source and destination of money flow, leading to money laundering and cases of bribery. This possible as NCPI maintains a mapper where each bank account is linked (only the latest one). However, Aadhaar number can be linked with multiple bank accounts of an individual. So when a transaction is made, the mapper records the transaction only from that 1 account. But if another transaction takes place with another bank account, that record is not maintained by the mapper at NCPI since it records only transactions of the latest account seeded in that. This makes money laundering easy as the money moves from aadhaar number to aadhaar number now rather than bank account to bank account.

Endnotes

[1] See: http://cis-india.org/internet-governance/events/uidai-and-welfare-services-exclusion-and-countermeasures-aug-27.

[2] See: http://cis-india.org/internet-governance/blog/report-on-understanding-aadhaar-and-its-new-challenges.

[3] See: https://uidai.gov.in/beta/images/the_aadhaar_act_2016.pdf.

[4] See: http://scroll.in/latest/816343/aadhaar-numbers-may-soon-be-compulsory-to-book-railway-tickets.

[5] See: http://www.thehindu.com/news/national/karnataka/linking-bpl-ration-card-with-aadhaar-made-mandatory/article9094935.ece.

[6] See: http://timesofindia.indiatimes.com/india/After-scam-Bihar-to-link-exams-to-Aadhaar/articleshow/54000108.cms.

[7] See: http://www.dailypioneer.com/state-editions/cs-calls-for-early-steps-to-link-aadhaar-to-ac.html.

[8] See: http://www.cag.gov.in/sites/default/files/audit_report_files/Union_Commercial_Compliance_Full_Report_25_2016_English.pdf.

[9] See: http://petroleum.nic.in/docs/lpg/LPG%20Control%20Order%20GSR%20718%20dated%2026.09.2011.pdf.

[10] See: http://judis.nic.in/temp/494201232392013p.txt.

[11] Section 8(4) of the Aadhaar Act, 2016 states that "The Authority shall respond to an authentication query with a positive, negative or any other appropriate response sharing such identity information excluding any core biometric information."

For more details visit https://cis-india.org/internet-governance/blog/workshop-report-uidai-and-welfare-services-august-27-2016

Right to Food Campaign, Ranchi Convention, 2016

sumandro — 2019-03-16T04:40:52Z

The Right to Food Campaign held its 2016 Convention in Ranchi during September 23-25, 2016. While three years have elapsed since the passage of the National Food Security Act, despite improvements in the Public Distribution System (PDS), large implementation gaps remain. This is what the Convention focused on, and gathered researchers and campaigners from across the country to share experiences and case studies on effectiveness and exclusions from the PDS. Sumandro Chattapadhyay took part in a session of the Convention to discuss how UID-linked welfare delivery is being rolled out across key programmes like provision of pension and rationed distribution of essential commodities, and their impact on people's right to welfare services.

Right to Food Campaign: Website.

Right to Food Campaign: Cash Transfers and UID: Our Main Demands.

Ranchi Convention, 2016: Programme.

For more details visit https://cis-india.org/internet-governance/news/right-to-food-campaign-ranchi-convention-2016

Comments on the Report of the Committee on Digital Payments (December 2016)

Sumandro Chattapadhyay and Amber Sinha — 2017-01-12T12:32:22Z

The Committee on Digital Payments constituted by the Ministry of Finance and chaired by Ratan P. Watal, Principal Advisor, NITI Aayog, submitted its report on the "Medium Term Recommendations to Strengthen Digital Payments Ecosystem" on December 09, 2016. The report was made public on December 27, and comments were sought from the general public. Here are the comments submitted by the Centre for Internet and Society.

1. Preliminary

1.1. This submission presents comments by the Centre for Internet and Society (“CIS”) [1] in response to the report of the Committee on Digital Payments, chaired by Mr. Ratan P. Watal, Principal Advisor, NITI Aayog, and constituted by the Ministry of Finance, Government of India (“the report”) [2].

2. The Centre for Internet and Society

2.1. The Centre for Internet and Society, CIS, is a non-profit organisation that undertakes interdisciplinary research on internet and digital technologies from policy and academic perspectives. The areas of focus include digital accessibility for persons with diverse abilities, access to knowledge, intellectual property rights, openness (including open data, free and open source software, open standards, and open access), internet governance, telecommunication reform, digital privacy, and cyber-security.

2.2. CIS is not an expert organisation in the domain of banking in general and payments in particular. Our expertise is in matters of internet and communication governance, data privacy and security, and technology regulation. We deeply appreciate and are most inspired by the Ministry of Finance’s decision to invite entities from both the sectors of finance and information technology. This submission is consistent with CIS’ commitment to safeguarding general public interest, and the interests and rights of various stakeholders involved, especially the citizens and the users. CIS is thankful to the Ministry of Finance for this opportunity to provide a general response on the report.

3. Comments

3.1. CIS observes that the decision by the Government of India to withdraw the legal tender character of the old high denomination banknotes (that is, Rs. 500 Rs. 1,000 notes), declared on November 08, 2016 [3], have generated unprecedented data about the user base and transaction patterns of digital payments systems in India, when pushed to its extreme use due to the circumstances. The majority of this data is available with the National Payments Corporation of India and the Reserve Bank of India. CIS requests the authorities concerned to consider opening up this data for analysis and discussion by public at large and experts in particular, before any specific policy and regulatory decisions are taken towards advancing digital payments proliferation in India. This is a crucial opportunity for the Ministry of Finance to embrace (open) data-driven regulation and policy-making.

3.2. While the report makes a reference to the European General Data Protection Directive, it does not make a reference to any substantive provisions in the Directive which may be relevant to digital payments. Aside from the recommendation that privacy protections around the purpose limitation principle be relaxed to ensure that payment service providers be allowed to process data to improve fraud monitoring and anti-money laundering services, the report is silent on significant privacy and data protection concerns posed by digital payments services. CIS strongly warns that the existing data protection and security regulations under Information Technology (Reasonable security practices and procedures and sensitive personal data or information), Rules are woefully inadequate in their scope and application to effectively deal with potential privacy concerns posed by digital payments applications and services. Some key privacy issues that must be addressed either under a comprehensive data protection legislation or a sector specific financial regulation are listed below. The process of obtaining consent must be specific, informed and unambiguous and through a clear affirmative action by the data subject based upon a genuine choice provided along with an option to opt out at any stage. The data subjects should have clear and easily enforceable right to access and correct their data. Further, data subjects should have the right to restrict the usage of their data in circumstances such as inaccuracy of data, unlawful purpose and data no longer required in order to fulfill the original purpose.

3.3. The initial recommendation of the report is to “[m]ake regulation of payments independent from the function of central banking” (page 22). This involves a fundamental transformation of the payment and settlement system in India and its regulation. We submit that a decision regarding transformation of such scale and implications is taken after a more comprehensive policy discussion, especially involving a wider range of stakeholders. The report itself notes that “[d]igital payments also have the potential of becoming a gateway to other financial services such as credit facilities for small businesses and low-income households” (page 32). Thus, a clear functional, and hence regulatory, separation between the (digital) payments industry and the lending/borrowing industry may be either effective or desirable. Global experience tells us that digital transactions data, along with other alternative data, are fast becoming the basis of provision of financial and other services, by both banking and non-banking (payments) companies. We appeal to the Ministry of Finance to adopt a comprehensive and concerted approach to regulating, enabling competition, and upholding consumers’ rights in the banking sector at large.

3.4. The report recognises “banking as an activity is separate from payments, which is more of a technology business” (page 154). Contemporary banking and payment businesses are both are primarily technology businesses where information technology particularly is deployed intimately to extract, process, and drive asset management decisions using financial transaction data. Further, with payment businesses (such as, pre-paid instruments) offering return on deposited money via other means (such as, cashbacks), and potentially competing and/or collaborating with established banks to use financial transaction data to drive lending decisions, including but not limited to micro-loans, it appears unproductive to create a separation between banking as an activity and payments as an activity merely in terms of the respective technology intensity of these sectors. CIS firmly recommends that regulation of these financial services and activities be undertaken in a technology-agnostic manner, and similar regulatory regimes be deployed on those entities offering similar services irrespective of their technology intensity or choice.

3.5. The report highlights two major shortcomings of the current regulatory regime for payments. Firstly “the law does not impose any obligation on the regulator to promote competition and innovation in the payments market” (page 153). It appears to us that the regulator’s role should not be to promote market expansion and innovation but to ensure and oversee competition. We believe that the current regulator should focus on regulating the existing market, and the work of the expansion of the digital payments market in particular and the digital financial services market in general be carried out by another government agency, as it creates conflict of interest for the regulator otherwise. Secondly, the report mentions that Payment and Settlement Systems Act does not “focus the regulatory attention on the need for consumer protection in digital payments” and then it notes that a “provision was inserted to protect funds collected from customers” in 2015 (page 153). This indicates that the regulator already has the responsibility to ensure consumer protection in digital payments. The purview and modalities of how this function of course needs discussion and changes with the growth in digital payments.

3.6. The report identifies the high cost of cash as a key reason for the government’s policy push towards digital payments. Further, it mentions that a “sample survey conducted in 2014 across urban and rural neighbourhoods in Delhi and Meerut, shows that despite being keenly aware of the costs associated with transacting in cash, most consumers see three main benefits of cash, viz. freedom of negotiations, faster settlements, and ensuring exact payments” (page 30). It further notes that “[d]igital payments have significant dependencies upon power and telecommunications infrastructure. Therefore, the roll out of robust and user friendly digital payments solutions to unelectrified areas/areas without telecommunications network coverage, remains a challenge.” CIS much appreciates the discussion of the barriers to universal adoption and rollout of digital payments in the report, and appeals to the Ministry of Finance to undertake a more comprehensive study of the key investments required by the Government of India to ensure that digital payments become ubiquitously viable as well as satisfy the demands of a vast range of consumers that India has. The estimates about investment required to create a robust digital payment infrastructure, cited in the report, provide a great basis for undertaking studies such as these.

3.7. CIS is very encouraged to see the report highlighting that “[w]ith the rising number of users of digital payment services, it is absolutely necessary to develop consumer confidence on digital payments. Therefore, it is essential to have legislative safeguards to protect such consumers in-built into the primary law.” We second this recommendation and would like to add further that financial transaction data is governed under a common data protection and privacy regime, without making any differences between data collected by banking and non-banking entities.

3.8. We are, however, very discouraged to see the overtly incorrect use of the word “Open Access” in this report in the context of a payment system disallowing service when the client wants to transact money with a specific entity [4]. This is not an uncommon anti-competitive measure adopted by various platform players and services providers so as to disallow users from using competing products (such as, not allowing competing apps in the app store controlled by one software company). The term “Open Access” is not only the appropriate word to describe the negation of such anti-competitive behaviour, its usage in this context undermines its accepted meaning and creates confusion regarding the recommendation being proposed by the report. The closest analogy to the recommendation of the report would perhaps be with the principle of “network neutrality” that stands for the network provider not discriminating between data packets being processed by them, either in terms of price or speed.

3.9. A major recommendation by the report involves creation of “a fund from savings generated from cash-less transactions … by the Central Government,” which will use “the trinity of JAM (Jan Dhan, Adhaar, Mobile) [to] link financial inclusion with social protection, contributing to improved Social and Financial Security and Inclusion of vulnerable groups/ communities” (page 160-161). This amounts to making Aadhaar a mandatory ID for financial inclusion of citizens, especially the marginal and vulnerable ones, and is in direct contradiction to the government’s statements regarding the optional nature of the Aadhaar ID, as well as the orders by the Supreme Court on this topic.

3.10. The report recommends that “Aadhaar should be made the primary identification for KYC with the option of using other IDs for people who have not yet obtained Aadhaar” (page 163) and further that “Aadhaar eKYC and eSign should be a replacement for paper based, costly, and shared central KYC registries” (page 162). Not only these measures would imply making Aadhaar a mandatory ID for undertaking any legal activity in the country, they assume that the UIDAI has verified and audited the personal documents submitted by Aadhaar number holders during enrollment. A mandate for replacement of the paper-based central KYC agencies will only remove a much needed redundancy in the the identity verification infrastructure of the government.

3.11. The report suggests that “[t]ransactions which are permitted in cash without KYC should also be permitted on prepaid wallets without KYC” (page 164-165). This seems to negate the reality that physical verification of a person remains one of the most authoritative identity verification process for a natural person, apart from DNA testing perhaps. Thus, establishing full equivalency of procedure between a presence-less transaction and one involving a physically present person making the payment will only amount to removal of relatively greater security precautions for the former, and will lead to possibilities of fraud.

3.12. In continuation with the previous point, the report recommends promotion of “Aadhaar based KYC where PAN has not been obtained” and making of “quoting Aadhaar compulsory in income tax return for natural persons” (page 163). Both these measures imply a replacement of the PAN by Aadhaar in the long term, and a sharp reduction in growth of new PAN holders in the short term. We appeal for this recommendation to be reconsidered as integration of all functionally separate national critical information infrastructures (such as PAN and Aadhaar) into a single unified and centralised system (such as Aadhaar) engenders massive national and personal security threats.

3.13. The report suggest the establishment of “a ranking and reward framework” to recognise and encourage for the best performing state/district/agency in the proliferation of digital payments. It appears to us that creation of such a framework will only lead to making of an environment of competition among these entities concerned, which apart from its benefits may also have its costs. For example, the incentivisation of quick rollout of digital payment avenues by state government and various government agencies may lead to implementation without sufficient planning, coordination with stakeholders, and precautions regarding data security and privacy. The provision of central support for digital payments should be carried out in an environment of cooperation and not competition.

3.14. CIS welcomes the recommendation by the report to generate greater awareness about cost of cash, including by ensuring that “large merchants including government agencies should account and disclose the cost of cash collection and cash payments incurred by them periodically” (page 164). It, however, is not clear to whom such periodic disclosures should be made. We would like to add here that the awareness building must simultaneously focus on making public how different entities shoulder these costs. Further, for reasons of comparison and evidence-driven policy making, it is necessary that data for equivalent variables are also made open for digital payments - the total and disaggregate cost, and what proportion of these costs are shouldered by which entities.

3.15. The report acknowledges that “[t]oday, most merchants do not accept digital payments” and it goes on to recommend “that the Government should seize the initiative and require all government agencies and merchants where contracts are awarded by the government to provide at-least one suitable digital payment option to its consumers and vendors” (page 165). This requirement for offering digital payment option will only introduce an additional economic barrier for merchants bidding for government contracts. We appeal to the Ministry of Finance to reconsider this approach of raising the costs of non-digital payments to incentivise proliferation of digital payments, and instead lower the existing economic and other barriers to digital payments that keep the merchants away. The adoption of digital payments must not lead to increasing costs for merchants and end-users, but must decrease the same instead.

3.16. As the report was submitted on December 09, 2016, and was made public only on December 27, 2016, it would have been much appreciated if at least a month-long window was provided to study and comment on the report, instead of fifteen days. This is especially crucial as the recently implemented demonetisation and the subsequent banking and fiscal policy decisions taken by the government have rapidly transformed the state and dynamics of the payments system landscape in India in general, and digital payments in particular.

Endnotes

[1] See: http://cis-india.org/.

[2] See: http://finmin.nic.in/reports/Note-watal-report.pdf and http://finmin.nic.in/reports/watal_report271216.pdf.

[3] See: http://finmin.nic.in/cancellation_high_denomination_notes.pdf.

[4] Open Access refers to “free and unrestricted online availability” of scientific and non-scientific literature. See: http://www.budapestopenaccessinitiative.org/read.

For more details visit https://cis-india.org/internet-governance/blog/comments-on-the-report-of-the-committee-on-digital-payments-dec-2016

New Media, personalisation and the role of algorithms

amber — 2017-01-16T07:20:52Z

In his much acclaimed book, The Filter Bubble, Eli Pariser explains how personalisation of services on the web works and laments that they are creating individual bubbles for each user, which run counter to the idea of the Internet as an inherently open place. While Pariser’s book looks at the practices of various large companies providing online services, he briefly touches upon the role of new media such as search engines and social media portals in new curation. Building upon Pariser’s unexplored argument, this article looks at the impact of algorithmic decision-making and Big Data in the context of news reporting and curation.

Everything which bars freedom and fullness of communication sets up barriers that divide human beings into sets and cliques, into antagonistic sects and factions, and thereby undermines the democratic way of life. —John Dewey

Eli Pariser, in his book, The Filter Bubble,[1] refers to the scholarship by Walter Lippmann and John Dewey as integral to the evolution of the understanding of the democratic and ethical duties of the Fourth Estate. Lippmann was disillusioned by the role of newspapers in propaganda for the First World War. He responded with three books in quick succession — Liberty and the News,[2] Public Opinion[3] and The Phantom Public.[4] Lippmann brought attention the fact that the process of news-reporting was conducted through privately determined and unexamined standards. The failure of the Fourth Estate to perform its democratic functions, was, in the opinion of Lippmann, one of the prime factors responsible for the public not being an informed and rational entity. John Dewey, while rejecting Lippmann’s arguments that matters of public policy can only be determined by inside experts with training and education, did acknowledge the his critique of the media.

Pariser points to the creation of a wall between editorial decisionmaking and advertiser interests, as the eventual result of the Lippmann and Dewey debate. While accepting that this division between the financial and reporting sides of media houses has not been always observed, Pariser emphasises that the fact that the standard exists is important.[5] Unlike traditional media, the new media which relies on algorithmic decision-making for personalisation is not subject to the same standards which try to mitigate the influence of commercial interests on editorial decisions while performing many of the same functions as the traditional media.[6]

How personalisation algorithms work

Kevin Slavin, at his famous talk in the TEDGLobal Conference, characterised algorithms as “maths that computers use to decide stuff” and that it was infiltrating every aspect of our lives.[7] According to Slavin’s view, algorithms can be seen as control technologies and shape our world constantly through media and information systems, dynamically modifying content and function through these programmed routines. Search engines and social media platforms perpetually rank user-generated content through algorithms.[8]

Personalisation technologies have various advantages. It translates into more relevant content, which for service providers means more clicks and revenue and for consumer, less time spent on finding the content.[9] However, it also leads to privacy compromise, lack of control and reduced individual capability.[10] Search engines like Google use the famous PageRank algorithm, which combined with geographical location and previous searches yields most relevant search results.[11] PageRank algorithm uses various real time variables dependent on both voluntary and involuntary user inputs. These variables include number of clicks, number of occurrences of the key terms and number of references by other credible pages etc. This data in turn determines the order of pages in search results and influences the way we perceive, understand and analyse information.[12] Maps showing real time traffic information retrieve data from laser and infrared sensors alongside the road and from information from devices of users. Once this real time data is combined with historical trends, these maps recommend rout to every user, hence influencing the traffic patterns.[13]

Even though this phenomenon of personalization may appears to be new, it has been prevalent in the society for ages.[14] The history of mass media culture clearly shows personalization has always been a method to increase market, market reach and customer satisfaction.[15] Newspapers have sections dedicated to special topics, radio and TV have channels dedicated to different interest groups, age groups and consumers.[16] These personalised sections in a newspaper and personalised channels on radio and television don’t just provide greater satisfaction to the readers or listeners or consumers, they also provide targeted advertisement space for the advertisers and content developers. However, digital footprints and mass collection of data have made this phenomenon much more granular and detailed. Geographical location of an individual can tell a lot about their community, their culture and other important traits local to a community.[17] This data further assists in personalisation. Current developments in technology not only help in better collection of data about personal preferences but also help in better personalisation.

Pariser mentions three ways in which the personalization technologies of this day are different from those of the past. First, for the very first time, individuals are alone in the filter bubble. While in traditional forms of personalisation, there were various individuals who shared the same frame of reference, now there is a separate sets of filters governing the dissemination of content to each individual.[18] Second, the personalisation technologies are entirely invisible now, and there is little that consumers can do to control or modify them.[19] Third, often the decision to be subject to these personalisation technologies is not an informed choice. A good example of this would be an individual’s geographical location.[20]

The neutrality of New Media?

More and more, we have noticed personalisation technologies having an impact on how we consume news on the Internet. Google News, Facebook’s News Feed which tries to put together a dynamic feed for both personal and global stories, and Twitter’s trending hashtag feature, have brought forward these services are key drivers of an emerging news ecosystem. Initially, this new media was hailed as a natural consequence of the Internet which would enable greater public participation, allow journalists to find more stories and engage with the readers directly. An illustration of the same could be seen in the way Internet based news media and social networking websites behaved in the aftermath of Israel’s attacks on a United Nations run school in Gaza strip. While much of the international Internet media covered the story, Israel’s home media did not cover the story. The only exception to this was the liberal Israeli news website Ha’aretz.[21] Network graph details of Twitter, for a few days immediately after the incident clearly show the social media manifestation of the event in the personalised cyberspace. It is clearly visible that when most of the word was re-tweeting news of this heinous act of Israel, Israeli’s hardly re-tweeted this news. In fact they were busty re-tweeting the news of rocket attacks on Israel.[22]

The use of social media in newsmaking was hailed by many scholars as symptomatic of the decentralisation characteristic of the Internet. It has been seen as movement towards greater grassroots participation by negating the ‘gatekeeping’ role traditionally played by editors. Thomas Poell and José van Dijck punch holes in theory of social media and other online technologies as mere facilitators of user participation and translators of user preferences through Big Data analytics.[23] They quote T. Gillespie’s work which talks of the narrative of these online services as platforms which are “open, neutral, egalitarian and progressive support for activity.”[24]

Pedro Domingos calls the overwhelming number of choices as the defining problem of the information age, and machine learning and data analytics as the largest part of this solution.[25] The primary function of algorithmic decision making in the context of consumption of content is to narrow down the choices. Domingos is more optimistic about the impact of these technologies, and he says “last step of the decision is usually still for humans to make, but learners intelligently reduce the choices to something a human can manage.”[26] On the other hand, Pariser is more circumspect about the coercive result of machine learning algorithms. Whichever way we lean, we have to accept that a large part of personalisation algorithms is to select and prioritize content by categorising it on the basis of relevance and popularity.

Poell and van Dijck call this a new knowledge logic which in effect replaces human judgement (as, earlier exercised by editors) to some kind of proxy decisionmaking based on data. Their main thesis is that there is little evidence to suggest that the latter is more democratic than former and creates new problems of its own. They go on to compare the practices of various services including Facebook’s new graph and Twitter’s trending topic, and conclude that they prioritise breaking news stories over other kinds of content.[27] For instance, the algorithm for the trending topics depends not on the volume but the velocity of the tweets with the hashtag or term. It could be argued that given this predilection, the algorithms will rarely prefer complex content. If we go by Lippmann and Dewey’s idea that the role of the Fourth Estate is to inform public debate and accountability of those in positions of power, this aspect of Big Data algorithms does not correspond with this role.

Quantified Audience

Another aspect of use of Big Data and algorithms in New Media that requires attention is that the networked infrastructure enables a quantified audience. C W Anderson who has studied newsroom practices in the US looked at role played by audience quantification and rationalization in shifting newswork practices. He concluded that more and more, journalists are less autonomous in their news decisions and increasingly reliant on audience metrics as a supplement to news judgment.[28] Poell and van Dijck review the the practices by some leading publications such a New York Times, L.A. Times and Huffington Post, and degree to which audience metrics dictates editorial decisions. While New York Times seems to prioritise content on their social media portals based on expectation of spike in user traffic, L.A. Times goes one step further by developing content specifically aimed towards promoting greater social participation. Neither of these practices though compare to the reliance on SEO and SMO strategies of web-born news providers like Huffington Post. They have traffic editors who trawl the Internet for trending topics and popular search terms, the feedback from them dictates the content creation.[29]

Conclusion

The above factors demonstrate that the idea of New Media leading to the Fourth Estate performing its democratic functions does not take into account the actual practices. This idea is based on the erroneous assumption that technology, in general and algorithms, in particular are neutral. While the emergence of New Media might have reduced the gatekeeping role played by the editors, its strong prioritisation of content that will be popular reduce the validity of arguments that it leads to more informed public discussion. As Pariser said, the traditional media scores over the New Media inasmuch as there is an existence of a standard of division between editorial decisionmaking and advertiser interest. While this standard is flouted by media houses all the time, it exists as a metric to aspire to and measure service providers against. The New Media performs many of the same functions and maybe it is time to evolve some principles and ethical standards that take into account the need for it to perform these democratic functions.

Endnotes

^{^[1]} Eli Pariser, The Filter Bubble: What the Internet is hiding from you (The Penguin Press, New York, 2011)

[2] Walter Lippmann, Liberty and News (Harcourt, Brace and Howe, New York 1920) available athttps://archive.org/details/libertyandnews01lippgoog

^{^[3]} Walter Lippmann, Public Opinion (Harcourt, Brace and Howe, New York 1920) available at http://xroads.virginia.edu/~Hyper2/CDFinal/Lippman/cover.html

^{^[4]} Walter Lippmann, The Phantom Public (Transaction Publishers, New York, 1925)

^{^[5]} Supra Note 1 at 35.

^{^[6]} Supra Note 1 at 36.

^{^[7]} https://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world/transcript?language=en

^{^[8]} Fenwick McKelvey, “Algorithmic Media Need Democratic Methods: Why Publics Matter”, available at http://www.fenwickmckelvey.com/wp-content/uploads/2014/11/2746-9231-1-PB.pdf.

^{^[9]} http://mashable.com/2011/06/03/filters-eli-pariser/#9tIHrpa_9Eq1

^{^[10]} Helen Ashman, Tim Brailsford, Alexandra Cristea, Quan Z Sheng, Craig Stewart, Elaine Torns and Vincent Wade, “The ethical and social implications of personalization technologies for e-learning” available at http://www.sciencedirect.com/science/article/pii/S0378720614000524.

^{^[11]} Sergey Brin and Lawrence Page, “The Anatomy of a Large-Scale Hypertextual Web Search Engine” available at http://infolab.stanford.edu/pub/papers/google.pdf.

^{^[12]} Ian Rogers, “The Google Pagerank Algorithm and How It Works” available at http://www.cs.princeton.edu/~chazelle/courses/BIB/pagerank.htm.

^{^[13]} Trygve Olson and Terry Nelson, “The Internet’s Impact on Political Parties and Campaigns”, available at http://www.kas.de/wf/doc/kas_19706-544-2-30.pdf?100526130942.

^{^[14]} Ian Witten, “Bias, privacy and and personalisation on the web”, available at http://www.cs.waikato.ac.nz/~ihw/papers/07-IHW-Bias,privacyonweb.pdf.

^{^[15]} Supra Note 1 at 10.

^{^[16]} https://www.americanpressinstitute.org/publications/reports/survey-research/social-demographic-differences-news-habits-attitudes/

^{^[17]} Charles Heatwole, “Culture: A Geographical Perspective” available at http://www.p12.nysed.gov/ciai/socst/grade3/geograph.html.

^{^[18]} Supra Note 1 at 10.

^{^[19]} Id.

^{^[20]} Supra Note 1 at 11.

^{^[21]} Paul Mason, “Why Israel is losing the social media war over Gaza?” available at http://blogs.channel4.com/paul-mason-blog/impact-social-media-israelgaza-conflict/1182.

^{^[22]} Gilad Lotan, Israel, Gaza, War & Data: Social Networks and the Art of Personalizing Propaganda available at www.huffingtonpost.com/entry/israel-gaza-war-social-networks-data_b_5658557.html

^{^[23]} Thomas Poell and José van Dijck, “Social Media and Journalistic Independence” in Media Independence: Working with Freedom or Working for Free?, edited by James Bennett & Niki Strange. (Routledge, London, 2015)

^{^[24]} T Gillespie, “The politics of ‘platforms,” in New Media & Society (Volume 12, Issue 3).

^{^[25]} Pedro Domingos, The Master Algorithm: How the quest for the ultimate learning machine will re-make the world (Basic Books, New York, 2015) at 38.

^{^[26]} Ibid at 40.

^{^[27]} Supra Note 23.

^{^[28]} C W Anderson, Between creative and quantified audiences: Web metrics and changing patterns of newswork in local US newsrooms, available at https://www.academia.edu/10937194/Between_Creative_And_Quantified_Audiences_Web_Metrics_and_Changing_Patterns_of_Newswork_in_Local_U.S._Newsrooms

^{^[29]} Supra Note 23.

For more details visit https://cis-india.org/internet-governance/new-media-personalisation-and-the-role-of-algorithms

Adoption of Standards in Smart Cities - Way Forward for India

vanya — 2016-04-11T03:04:46Z

With a paradigm shift towards the concept of “Smart Cities’ globally, as well as India, such cities have been defined by several international standardization bodies and countries, however, there is no uniform definition adopted globally. The glue that allows infrastructures to link and operate efficiently is standards as they make technologies interoperable and efficient.

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Globally, the pace of urbanization is increasing exponentially. The world’s urban population is projected to rise from 3.6 billion to 6.3 billion between 2011 and 2050. A solution for the same has been development of sustainable cities by improving efficiency and integrating infrastructure and services [1]. It has been estimated that during the next 20 years, 30 Indians will leave rural India for urban areas every minute, necessitating smart and sustainable cities to accommodate them [2]. The Smart Cities Mission of the Ministry of Urban Development was announced in the year 2014, followed by selection of 100 cities in the year 2015 and 20 of them being selected for the first Phase of the project in the year 2016. The Mission [3] lists the “core infrastructural elements” that a smart city would incorporate like adequate water supply, assured electricity, sanitation, efficient public transport, affordable housing (especially for the poor), robust IT connectivity and digitisation, e-governance and citizen participation, sustainable environment, safety and security for citizens, health and education.

With a paradigm shift towards the concept of “Smart Cities’ globally, as well as India, such cities have been defined by several international standardization bodies and countries, however, there is no uniform definition adopted globally. The envisioned modern and smart city promises delivery of high quality services to the citizens and will harness data capture and communication management technologies. The performance of such cities would be monitored on the basis of physical as well as the social structure comprising of smart approaches and solution to utilities and transport.

The glue that allows infrastructures to link and operate efficiently is standards as they make technologies interoperable and efficient. Interoperability is essential and to ensure smart integration of various systems in a smart city, internationally agreed standards that include technical specifications and classifications must be adhered to. Development of international standards ensure seamless interaction between components from different suppliers and technologies [4].

Standardized indicators within standards benefit smart cities in the following ways:

Effective governance and efficient delivery of services.
International and Local targets, benchmarking and planning.
Informed decision making and policy formulation.
Leverage for funding and recognition in international entities.
Transparency and open data for investment attractiveness.
A reliable foundation for use of big data and the information explosion to assist cities in building core knowledge for city decision-making, and enable comparative insight.

The adoption of standards for smart cities has been advocated across the world as they are perceived to be an effective tool to foster development of the cities. The Director of the ITU Telecommunication Standardization Bureau Chaesub Lee is of the view that “Smart cities will employ an abundance of technologies in the family of the Internet of Things (IoT) and standards will assist the harmonized implementation of IoT data and applications , contributing to effective horizontal integration of a city’s subsystems” [5].

Smart Cities standards in India

National Association of Software and Services Companies (NASSCOM) partnered with Accenture [6] to prepare a report called ‘Integrated ICT and Geospatial Technologies Framework for 100 Smart Cities Mission’ [7] to explore the role of ICT in developing smart cities [8], after the announcement of the Mission by Indian Government. The report, released in May 2015, lists down 55 global standards, keeping in view several city sub-systems like urban planning, transport, governance, energy, climate and pollution management, etc which could be applicable to the smart cities in India.

Though NASSCOM is working closely with the Ministry of Urban Development to create a sustainable model for smart cities [9], due to lack of regulatory standards for smart cities, the Bureau of Indian Standards (BIS) in India has undertaken the task to formulate standardised guidelines for central and state authorities in planning, design and construction of smart cities by setting up a technical committee under the Civil engineering department of the Bureau. However, adoption of the standards by implementing agencies would be voluntary and intends to complement internationally available documents in this area [10].

Developing national standards in line with these international standards would enable interoperability (i.e. devices and systems working together) and provide a roadmap to address key issues like data protection, privacy and other inherent risks in the digital delivery and use of public services in the envisioned smart cities, which call for comprehensive data management standards in India to instill public confidence and trust [11].

Key International Smart Cities Standards

Following are the key internationally accepted and recognized Smart Cities standards developed by leading organisations and the national standardization bodies of several countries that India could adopt or develop national standards in line with these.

The International Organization for Standardization (ISO) - Smart Cities Standards

ISO is an instrumental body advocating and developing for smart cities to safeguard rights of the people against a liveable and sustainable environment. The ISO Smart Cities Strategic Advisory Group uses the following working definition: A ‘Smart City’ is one that dramatically increases the pace at which it improves its social, economic and environmental (sustainability) outcomes, responding to challenges such as climate change, rapid population growth, and political and economic instability by fundamentally improving how it engages society, how it applies collaborative leadership methods, how it works across disciplines and city systems, and how it uses data information and modern technologies in order to transform services and quality of life for those in and involved with the city (residents, businesses, visitors), now and for the foreseeable future, without unfair disadvantage of others or degradation of the natural environment. [For details see ISO/TMB Smart Cities Strategic Advisory Group Final Report, September 2015 ( ISO Definition, June 2015)].

The ISO Technical Committee 268 works on standardization in the field of Sustainable Development in Communities [12] to encourage the development and implementation of holistic, cross-sector and area-based approaches to sustainable development in communities. The Committee comprises of 3 Working Groups [13]:

Working Group 1: System Management ISO 37101- This standard sets requirements, guidance and supporting techniques for sustainable development in communities. It is designed to help all kinds of communities manage their sustainability, smartness and resilience to improve the contribution of communities to sustainable development and assess their performance in this area [14].
Working Group 2 : City Indicators- The key Smart Cities Standards developed by ISO TC 268 WG 2 (City Indicators) are:

ISO 37120 Sustainable Development of Communities — Indicators for City Services and Quality of Life

One of the key standards and an important step in this regard was ISO 37120:2014 under the ISO’s Technical Committee 268 (See Working on Standardization in the field of Sustainable Development in Communities) providing clearly defined city performance indicators (divided into core and supporting indicators) as a benchmark for city services and quality of life, along with a standard approach for measuring each for city leaders and citizens [15]. The standard is global in scope and can help cities prioritize city budgets, improve operational transparency, support open data and applications [16]. It follows the principles [17] set out and can be used in conjunction with ISO 37101.

ISO 37120 was the first ISO Standard on Global City Indicators published in the year 2014, developed on the basis of a set of indicators developed and extensively tested by the Global City Indicators Facility (a project by University of Toronto) and its 250+ member cities globally. GCIF is committed to build standardized city indicators for performance management including a database of comparable statistics that allow cities to track their effectiveness on everything from planning and economic growth to transportation, safety and education [18].

The World Council on City Data (WCCD) [19] - a sister organization of the GCI/GCIF - was established in the year 2014 to operationalize ISO 37120 across cities globally. The standards encompasses 100 indicators developed around 17 themes to support city services and quality of life, and is accessible through the WCCD Open City Data Portal which allows for cutting-edge visualizations and comparisons. Indian cities are not yet listed with WCCD [20].

The indicators are listed under the following heads [21]:

Economy
Education
Environment
Energy
Finance
Fire and Emergency Responses
Governance
Health
Safety
Shelter
Recreation
Solid Waste
Telecommunication and innovation
Transportation
Urban Planning
Waste water
Water and Sanitation

This International Standard is applicable to any city, municipality or local government that undertakes to measure its performance in a comparable and verifiable manner, irrespective of size and location or level of development. City indicators have the potential to be used as critical tools for city managers, politicians, researchers, business leaders, planners, designers and other professionals [22]. The WCCD forum highlights need for cities to have a set of globally standardized indicators to [23]:

Manage and make informed decisions through data analysis
Benchmark and target
Leverage Funding with senior levels of government
Plan and establish new frameworks for sustainable urban development
Evaluate the impact of infrastructure projects on the overall performance of a city.

ISO/DTR 37121- Inventory and Review of Existing Indicators on Sustainable Development and Resilience in Cities

The second standard under ISO TC 268 WG 2 is ISO 37121, which defines additional indicators related to sustainable development and resilience in cities. Some of the indicators include: Smart Cities, Smart Grid, Economic Resilience, Green Buildings, Political Resilience, Protection of biodiversity, etc. The complete list can be viewed on the Resilient Cities website [24].

Working Group 3: Terminology - There are no publicly available documents so far, giving details about the status of the activities of this group. The ISO Technical Committee 268 also includes Sub Committee 1 (Smart Community Infrastructure) [25], comprising of the following Working Groups: 1) WG 1 Infrastructure metrics, and 2) WG 2 Smart Community Infrastructure.

The key Smart Cities Standards developed by ISO under this are:

ISO 37151:2015 Smart community infrastructures — Principles and Requirements for Performance Metrics
In the year 2015, a new ISO technical specification for smart cities- 37151:2015 for Principles and requirements for performance metrics was released. The purpose of standardization in the field of smart community infrastructures such as energy, water, transportation, waste, information and communications technology (ICT), etc. is to promote the international trade of community infrastructure products and services and improve sustainability in communities by establishing harmonized product standards [26]. The metrics in this standard will support city and community managers in planning and measuring performance, and also compare and select procurement proposals for products and services geared at improving community infrastructures [27].
This Technical Specification gives principles and specifies requirements for the definition,identification, optimization, and harmonization of community infrastructure performance metrics, and gives recommendations for analysis, regarding interoperability, safety, security of community infrastructures [28]. This new Technical Specification supports the use of the ISO 37120 [29].
ISO/TR 37150:2014 Smart Community Infrastructures - Review of Existing Activities Relevant to Metrics
This standard addresses community infrastructures such as energy, water, transportation, waste and information and communications technology (ICT). Smart community infrastructures take into consideration environmental impact, economic efficiency and quality of life by using information and communications technology (ICT) and renewable energies to achieve integrated management and optimized control of infrastructures. Integrating smart community infrastructures for a community helps improve the lifestyles of its citizens by, for example: reducing costs, increasing mobility and accessibility, and reducing environmental pollutants.
ISO/TR 37150 reviews relevant metrics for smart community infrastructures and provides stakeholders with a better understanding of the smart community infrastructures available around the world to help promote international trade of community infrastructure products and give information about leading-edge technologies to improve sustainability in communities [30]. This standard, along with the above mentioned standards [31] supports the multi-billion dollar smart cities technology industry.

Several other ISO Working Groups developing standards applicable to smart and sustainable cities have been listed in our website [32].

The International Telecommunications Union (ITU)

The ITU is another global body working on development of standards regarding smart cities.

A study group was formed in the year 2015 to tackle standardization requirements for the Internet of Things, with an initial focus on IoT applications in smart cities to address urban development challenges [33], to enable the coordinated development of IoT technologies, including machine-to-machine communications and ubiquitous sensor networks. The group is titled “ITU-T Study Group 20: IoT and its applications, including smart cities and communities”, established to develop standards that leverage IoT technologies to address urban-development challenges and the mechanisms for the interoperability of IoT applications and datasets employed by various vertically oriented industry sectors [34].

ITU-T also concluded a focused study group looking at smart sustainable cities in May 2015, acting as an open platform for smart city stakeholders to exchange knowledge in the interests of identifying the standardized frameworks needed to support the integration of ICT services in smart cities. Its parent group is ITU-T Study Group 5, which has agreed on the following definition of a Smart Sustainable City:
"A smart sustainable city is an innovative city that uses information and communication technologies (ICTs) and other means to improve quality of life, efficiency of urban operation and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social, environmental as well as cultural aspects".

UK - British Standards Institution

Apart from the global standards setting organisations, many countries have been looking at developing standards to address the growth of smart cities across the globe. In the UK, the British Standards Institution (BSI) has been commissioned by the UK Department of Business, Innovation and Skills (BIS) to conceive a Smart Cities Standards Strategy to identify vectors of smart city development where standards are needed. The standards would be developed through a consensus-driven process under the BSI to ensure good practise is shared between all the actors. The BIS launched the City's Standards Institute to bring together cities and key industry leaders and innovators to work together in identifying the challenges facing cities, providing solutions to common problems and defining the future of smart city standards [35].

PAS 181 Smart city framework- Guide to establishing strategies for smart cities and communities establishes a good practice framework for city leaders to develop, agree and deliver smart city strategies that can help transform their city’s ability to meet challenges faced in the future and meet the goals. The smart city framework (SCF) does not intend to describe a one-size-fits-all model for the future of UK cities but focuses on the enabling processes by which the innovative use of technology and data, together with organizational change, can help deliver the diverse visions for future UK cities in more efficient, effective and sustainable ways [36].
PD 8101 Smart cities- Guide to the role of the planning and development process gives guidance regarding planning for new development for smart city plans and provides an overview of the key issues to be considered and prioritized. The document is for use by local authority planning and regeneration officers to identify good practice in a UK context, and what tools they could use to implement this good practice. This aims to enable new developments to be built in a way that will support smart city aspirations at minimal cost [37].
PAS 182 Smart city concept model. Guide to establishing a model for data establishes an interoperability framework and data-sharing between agencies for smart cities for the following purposes:
1. To have a city where information can be shared and understood between organizations and people at each level
2. The derivation of data in each layer can be linked back to data in the previous layer
3. The impact of a decision can be observed back in operational data. The smart city concept model (SCCM) provides a framework that can normalize and classify information from many sources so that data sets can be discovered and combined to gain a better picture of the needs and behaviours of a city’s citizens (residents and businesses) to help identify issues and devise solutions. PAS 182 is aimed at organizations that provide services to communities in cities, and manage the resulting data, as well as decision-makers and policy developers in cities [38].
PAS 180 Smart cities Vocabulary helps build a strong foundation for future standardization and good practices by providing an industry-agreed understanding of smart city terms and definitions to be used in the UK. It provides a working definition of a Smart City- “Smart Cities” is a term denoting the effective integration of physical, digital and human systems in the built environment to deliver a sustainable, prosperous and inclusive future for its citizens [39]. This aims to help improve communication and understanding of smart cities by providing a common language for developers, designers, manufacturers and clients. The standard also defines smart city concepts across different infrastructure and systems’ elements used across all service delivery channels and is intended for city authorities and planners, buyers of smart city services and solutions [40], as well as product and service providers.

Endnotes

[1] See: http://www.iec.ch/whitepaper/pdf/iecWP-smartcities-LR-en.pdf.

[2] See: http://www.ibm.com/smarterplanet/in/en/sustainable_cities/ideas/.

[3] See: http://www.thehindubusinessline.com/economy/smart-cities-mission-welcome-to-tomorrows-world/article8163690.ece.

[4] See: http://www.iec.ch/whitepaper/pdf/iecWP-smartcities-LR-en.pdf.

[5] See: http://www.iso.org/iso/news.htm?refid=Ref2042.

[6] See: http://www.livemint.com/Companies/5Twmf8dUutLsJceegZ7I9K/Nasscom-partners-Accenture-to-form-ICT-framework-for-smart-c.html.

[7] See: http://www.nasscom.in/integrated-ict-and-geospatial-technologies-framework-100-smart-cities-mission.

[8] See: http://www.cxotoday.com/story/nasscom-creates-framework-for-smart-cities-project/.

[9] See: http://www.livemint.com/Companies/5Twmf8dUutLsJceegZ7I9K/Nasscom-partners-Accenture-to-form-ICT-framework-for-smart-c.html.

[10] See: http://www.business-standard.com/article/economy-policy/in-a-first-bis-to-come-up-with-standards-for-smart-cities-115060400931_1.html.

[11] See: http://www.longfinance.net/groups7/viewdiscussion/72-financing-financing-tomorrow-s-cities-how-standards-can-support-the-development-of-smart-cities.html?groupid=3.

[12] See: http://www.iso.org/iso/iso_technical_committee?commid=656906.

[13] See: http://cityminded.org/wp-content/uploads/2014/11/Patricia_McCarney_PDF.pdf.

[14] See: http://www.iso.org/iso/news.htm?refid=Ref1877.

[15] See: http://smartcitiescouncil.com/article/new-iso-standard-gives-cities-common-performance-yardstick.

[16] See: http://smartcitiescouncil.com/article/dissecting-iso-37120-why-new-smart-city-standard-good-news-cities.

[17] See: http://www.iso.org/iso/catalogue_detail?csnumber=62436.

[18] See: http://www.cityindicators.org/.

[19] See: http://www.dataforcities.org/.

[20] See: http://news.dataforcities.org/2015/12/world-council-on-city-data-and-hatch.html.

[21] See: http://news.dataforcities.org/2015/12/world-council-on-city-data-and-hatch.html.

[22] See: http://www.iso.org/iso/37120_briefing_note.pdf.

[23] See: http://www.dataforcities.org/wccd/.

[24] See: http://resilient-cities.iclei.org/fileadmin/sites/resilient-cities/files/Webinar_Series/HERNANDEZ_-_ICLEI_Resilient_Cities_Webinar__FINAL_.pdf.

[25] See: http://www.iso.org/iso/iso_technical_committee?commid=656967.

[26] See: https://www.iso.org/obp/ui/#iso:std:iso:ts:37151:ed-1:v1:en.

[27] See: http://www.iso.org/iso/home/news_index/news_archive/news.htm?refid=Ref2001&utm_medium=email&utm_campaign=ISO+Newsletter+November&utm_content=ISO+Newsletter+November+CID_4182720c31ca2e71fa93d7c1f1e66e2f&utm_source=Email%20marketing%20software&utm_term=Read%20more.

[28] See: http://www.iso.org/iso/37120_briefing_note.pdf.

[29] See: http://standardsforum.com/isots-37151-smart-cities-metrics/.

[30] See: http://www.iso.org/iso/executive_summary_iso_37150.pdf.

[31] See: http://standardsforum.com/isots-37151-smart-cities-metrics/.

[32] See: http://cis-india.org/internet-governance/blog/database-on-big-data-and-smart-cities-international-standards.

[33] See: http://smartcitiescouncil.com/article/itu-takes-internet-things-standards-smart-cities.

[34] See: https://www.itu.int/net/pressoffice/press_releases/2015/22.aspx.

[35] See: http://www.bsigroup.com/en-GB/smart-cities/.

[36] See: http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-181-smart-cities-framework/.

[37] See: http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PD-8101-smart-cities-planning-guidelines/.

[38] See: http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-182-smart-cities-data-concept-model/.

[39] See: http://www.iso.org/iso/smart_cities_report-jtc1.pdf.

[40] See: http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-180-smart-cities-terminology/.

For more details visit https://cis-india.org/internet-governance/blog/adoption-of-standards-in-smart-cities-way-forward-for-india

Exploring Big Data for Development: An Electricity Sector Case Study from India

sumandro — 2019-03-16T04:33:15Z

This working paper by Ritam Sengupta, Dr. Richard Heeks, Sumandro Chattapadhyay, and Dr. Christopher Foster draws from the field study undertaken by Ritam Sengupta, and is published by the Global Development Institute, University of Manchester. The field study was commissioned by the CIS, with support from the University of Manchester and the University of Sheffield.

Download the working paper: PDF

Abstract

This paper presents exploratory research into “data-intensive development” that seeks to inductively identify issues and conceptual frameworks of relevance to big data in developing countries. It presents a case study of big data innovations in “Stelcorp”; a state electricity corporation in India. In an attempt to address losses in electricity distribution, Stelcorp has introduced new digital meters throughout the distribution network to capture big data, and organisation-wide information systems that store and process and disseminate big data.

Emergent issues are identified across three domains: implementation, value and outcome. Implementation of big data has worked relatively well but technical and human challenges remain. The advent of big data has enabled some – albeit constrained – value addition in all areas of organisational operation: customer billing, fault and loss detection, performance measurement, and planning. Yet US$ tens of millions of investment in big data has brought no aggregate improvement in distribution losses or revenue collection. This can be explained by the wider outcome, with big data faltering in the face of external politics; in this case the electoral politics of electrification. Alongside this reproduction of power, the paper also reflects on the way in which big data has enabled shifts in the locus of power: from public to private sector; from labour to management; and from lower to higher levels of management.

A number of conceptual frameworks emerge as having analytical power in studying big data and global development. The information value chain model helps track both implementation and value-creation of big data projects. The design-reality gap model can be used to analyse the nature and extent of barriers facing big data projects in developing countries. And models of power – resource dependency, epistemic models, and wider frameworks – are all shown as helping understand the politics of big data.

Cross-posted from University of Manchester.

For more details visit https://cis-india.org/raw/exploring-big-data-for-development-an-electricity-sector-case-study-from-india

Call for Proposal: Big Data for Development – Initial Field Studies

sumandro — 2016-04-28T07:28:23Z

The Centre for Internet and Society, as part of a project with the University of Manchester and University of Sheffield, is inviting calls from researchers to undertake a brief initial study of a specific instance of use of big data for development in India. This is an exercise to build preliminary understanding of the landscape of big data for development in India, identify key research questions and priorities, and start developing connections with researchers interested in the field. The studies will be 6 weeks long - running from May to June 2016 - and the researchers are expected to produce a 3,000 words long report. We will support three field studies.

Study Process and Deliverable

The researcher is expected to propose and undertake a 6 weeks long study – starting from May 09 and ending on June 17 – of an instance of big data is being used to inform, target, operationalise, monitor, or support developmental and/or humanitarian activity in India.

During this period, the researcher is expected to interview 4-5 persons directly involved in the big data for development project concerned, and 2-3 other persons to get a wider sense of the context of the project.

By the end of the 6 weeks period, the researcher is expected to submit a 3,000 words long report. The report will be commented upon by Prof. Richard Heeks (University of Manchester), Dr. Christopher Foster (University of Sheffield), and Sumandro Chattapadhyay (CIS), and revised accordingly during the last weeks of June.

The individual reports will be published independently and as part of the larger project report, under Creative Commons Attribution 4.0 International license. The authors will be attributed appropriately.

All researchers will take part in a work-in-progress meeting (held over internet) during last week of May or first week of June.

Research Questions

The interviews will focus on the following topics:

Innovation: What is the nature of the innovation being done by the use of big data? What technical systems and/or applications are being deployed and replaced/superceded? Who are key actors in this innovation process?
Implementation: What is the grounded experience of implementing the big data technology? What are the key enablers and constraints being faced, both in the data collection stage, and the analysis and decision making stage?
Value: What is the value being created, and how is it understood? Is it organisational value, or socio-economic value? Who is gaining this value?
Ethics: What ethical concerns are emerging? Do they involve concerns about data quality, representation, privacy, or security? Is there concerns about a data divide being created among people who are represented in data and who are not, or among people who can gain value from the data and who cannot?

Application, Eligibility, and Remuneration

Please submit the following documents to apply:

Proposal: A one page note on the big data for development project that you would like to study. Please share a brief description of the project and how you will study it, including the name/designation of key people you will speak to.
Writing Sample: An article or a collection of articles, of not more than 8,000 words length in total.
CV: A short CV, two pages or less.

Please e-mail the documents to raw[at]cis-india[dot]org by Wednesday, May 04, 2016.

There is no eligibility criteria for submitting proposals. However, we will prioritise researchers living and studying big data for development projects in non X-class cities, that is in cities other than Ahmedabad, Bangalore, Chennai, Delhi, Hyderabad, Kolkata, Mumbai, and Pune.

We will select three researchers, and will offer Rs. 35,000 to each of them for this study. The amount will be paid in a single installment, after the draft field study report is submitted for comments.

For more details visit https://cis-india.org/jobs/call-for-proposal-big-data-for-development-field-studies

Big Data in the Global South International Workshop

praskrishna — 2015-11-06T02:04:49Z

Institute for Technology and Society of Rio de Janeiro welcomes you to an international workshop on Big Data at Hotel Windsor Florida, Rua Ferreira Viana, Flamengo, Rio de Janeiro, Brazil on November 16 and 17, 2015. Open Society Foundations and British Embassy Brasilia are sponsors for the event. The Centre for Internet & Society (CIS) is a research partner. Sunil Abraham, Pranesh Prakash and Vipul Kharbanda will be speaking at this event.

The event will bring together key representatives from government, civil society, the business sector and academia from Brazil, India, United Kingdom and several other countries. This is a closed multistakeholder round-table to discuss and map international examples of Big Data uses and regulation, both by private and public sectors, in order to develop practical strategies to promote adoption of harmonized rules by different actors. The event will also map existing initiatives involving the use of Big Data and present the results of a joint research initiative conducted by ITS and CIS in this field.

Resources

For more details visit https://cis-india.org/internet-governance/events/big-data-in-the-global-south-international-workshop

Centre for Internet and Society

Database on Big Data and Smart Cities International Standards

[45] SPRING Singapore Supported Close to 600 Companies in Standards Adoption, and Service Excellence Projects , 12th August 2015, http://www.spring.gov.sg/NewsEvents/PR/Pages/Internet-of-Things-(IoT)-Standards-Outline-to-Support-Smart-Nation-Initiative-Unveiled-20150812.aspx

Curating Genderlog India's Twitter handle

Seminar on Understanding Financial Technology, Cashless India, and Forced Digitalisation (Delhi, January 24)

Programme Schedule

09.30 - Registration

10:00 - Introduction to the Seminar & Setting the Context

10:15–11:30 - Session 1 - Understanding the Political Context of FinTech

11:30-11:45 – Tea / Coffee break

11:45-13:15 - Session 2 - How will FinTech Impact the Poor, and Labour and Banking Sector?

13:15-14:00 – Lunch

14:00-15:30 - Session 3 - Understanding the Economic Context of FinTech

15:30-17:00 - Session 4 - Understanding the Architecture of FinTech: Linkages to Aadhaar, IndiaStack etc

17:00 – Tea

Vidhi Doshi - Fingerprint Payments Prompt Privacy Fears in India (The Guardian)

Privacy fears

The Technology behind Big Data

Download the Paper (PDF, 277 kb)

Introduction

Data Acquisition

Data Sensing

Born Digital Data

Sensor Data

Data Collection & Storage

Non-relational databases

In-Memory Databases

Hybrid Systems

Apache Hadoop

Data Awareness

Data Processing & Analytics

MapReduce

Data Governance

Conclusion

Can data ever know who we really are?

Please read the full essay on Deep Dives: Can data ever know who we really are?

Zara Rahman: The Engine Room, Website, and Twitter

CFI-ACCION - Panel Discussion on 'Big Data: Challenge or Opportunity?' (Delhi, December 06)

Inclusive Finance India Summit: http://inclusivefinanceindia.org/.

Workshop Report - UIDAI and Welfare Services: Exclusion and Countermeasures

Introduction

Implementation of the UID Project

Concerns Arising from the Report of the Comptroller and Auditor General of India (CAG) on Implementation of PAHAL (DBTL) Scheme

UID-linked Welfare Delivery in Rajasthan

Aadhaar and e-KYC

Endnotes

Right to Food Campaign, Ranchi Convention, 2016

Right to Food Campaign: Website.

Right to Food Campaign: Cash Transfers and UID: Our Main Demands.

Ranchi Convention, 2016: Programme.

Comments on the Report of the Committee on Digital Payments (December 2016)

1. Preliminary

2. The Centre for Internet and Society

3. Comments

Endnotes

New Media, personalisation and the role of algorithms

How personalisation algorithms work

The neutrality of New Media?

Quantified Audience

Conclusion

Endnotes

Adoption of Standards in Smart Cities - Way Forward for India

Smart Cities standards in India

Key International Smart Cities Standards

The International Organization for Standardization (ISO) - Smart Cities Standards

ISO 37120 Sustainable Development of Communities — Indicators for City Services and Quality of Life

ISO/DTR 37121- Inventory and Review of Existing Indicators on Sustainable Development and Resilience in Cities

The International Telecommunications Union (ITU)

UK - British Standards Institution

Endnotes

Exploring Big Data for Development: An Electricity Sector Case Study from India

Download the working paper: PDF

Abstract

Call for Proposal: Big Data for Development – Initial Field Studies

Study Process and Deliverable

Research Questions

Application, Eligibility, and Remuneration

Big Data in the Global South International Workshop

Resources

^{^[45]} SPRING Singapore Supported Close to 600 Companies in Standards Adoption, and Service Excellence Projects , 12th August 2015, http://www.spring.gov.sg/NewsEvents/PR/Pages/Internet-of-Things-(IoT)-Standards-Outline-to-Support-Smart-Nation-Initiative-Unveiled-20150812.aspx