Prioritizing Digital Identity Goals - The Case Study of Aadhaar in India

Identity is one of the basic building blocks of the Fourth Industrial Revolution, and as the capability of digital technologies improves drastically in the last decade, identity in digital form has become unavoidable. Identity entitles an individual to various services like voting, education, employment, insurance, healthcare etc. Yet there are around 1 billion people in the world at present that do not possess any form of official identity. Lack of identity has a significant impact on people living in rural areas, especially women, children, and financially backward families. In recently released Sustainable Development Goal-16 by the UN, it has been recommended that by 2030, every individual should be given a legal identity. India’s digital identity program –Aadhaar is one significant contribution in this direction considering its coverage. Rolling out a national identity scheme needs a considerable budget, time and most importantly, domain knowledge for smooth implementation. This paper attempts to identify the overarching goals of Aadhaar. The study also ranks goals based on their significance. The research uses focus group for data collection along with secondary data. The research in total identified nine primary goals with uniqueness, privacy and security as the high priority goals and scalability and future-proofing of technology as low priority goals. Total Interpretive Structural Modeling (TISM) has been used to identify the significance of each goal. This study could be taken as a starting point by other nations that are desirous of having a similar biometric identity program for its citizens.

[1]  Martha A. Avila-Maravilla,et al.  Convergence or Conflict?: Digital Identities vs. Citizenship Rights: Case Study of Unique Identification Number, Aadhaar, in India , 2018, ICEGOV.

[2]  Jonathan T. Weinberg,et al.  Biometric identity , 2015, Commun. ACM.

[3]  H. Kumar,et al.  A policy framework for city eligibility analysis: TISM and fuzzy MICMAC-weighted approach to select a city for smart city transformation in India , 2019, Land Use Policy.

[4]  YeohWilliam,et al.  Extending the understanding of critical success factors for implementing business intelligence systems , 2016 .

[5]  Jeremy Rifkin,et al.  The Age of Access: The New Culture of Hypercapitalism Where All of Life Is a Paid-For Experience , 2001 .

[6]  Richard A. Krueger,et al.  Focus groups : a practical guide for applied research / by Richard A. Krueger , 1989 .

[7]  Arpan Kumar Kar,et al.  Assessment of Open Government Data Initiative - A Perception Driven Approach , 2017, I3E.

[8]  Roger Dunn,et al.  The development of a benchmarking tool for monitoring progress towards sustainable transportation in New Zealand , 2011 .

[9]  J. Rockart,et al.  A primer on critical success factors , 1981 .

[10]  P. Vigneswara Ilavarasan,et al.  Assessment of e-Governance Projects: an Integrated Framework and its Validation , 2017, ICEGOV '17.

[11]  William Yeoh,et al.  Extending the understanding of critical success factors for implementing business intelligence systems , 2016, J. Assoc. Inf. Sci. Technol..

[12]  Amiya Bhatia,et al.  India’s Aadhaar scheme and the promise of inclusive social protection , 2017 .

[13]  Frank Bannister,et al.  The Hong Kong e-Identity Card: Examining the Reasons for Its Success When Other Cards Continue to Struggle , 2015, Inf. Syst. Manag..

[14]  Herbert Kubicek,et al.  Different countries-different paths extended comparison of the introduction of eIDs in eight European countries , 2010 .

[15]  Harjit Singh,et al.  Performance Assessment of e-Government Projects: a Multi-Construct, Multi-Stakeholder Perspective , 2017, ICEGOV.

[16]  Arpan Kumar Kar,et al.  Critical Success Factors to Establish 5G Network in Smart Cities: Inputs for Security and Privacy , 2017, J. Glob. Inf. Manag..

[17]  Alfredo Pérez-Rueda,et al.  Determinants of multi-service smartcard success for smart cities development: A study based on citizens' privacy and security perceptions , 2015, Gov. Inf. Q..

[18]  Ali M. Al-Khouri,et al.  Digital identity: Transforming GCC economies , 2014 .

[19]  Umesh Hodeghatta Rao,et al.  Physical Security and Biometrics , 2014 .

[20]  Dana Marohn,et al.  Biometrics in healthcare , 2006 .

[21]  Elisa Bertino,et al.  Privacy-preserving Digital Identity Management for Cloud Computing , 2009, IEEE Data Eng. Bull..

[22]  Sushil Interpreting the Interpretive Structural Model , 2012 .

[23]  Arun Ross,et al.  An introduction to biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[24]  J. Sharma,et al.  Torture Redress Mechanism in Nepal and Bangladesh: a comparative perspective , 2017 .

[25]  Arpan Kumar Kar,et al.  Regulation and governance of the Internet of Things in India , 2018, Digital Policy, Regulation and Governance.

[26]  Rameshwar Dubey,et al.  Identification of Flexible Manufacturing System Dimensions and Their Interrelationship Using Total Interpretive Structural Modelling and Fuzzy MICMAC Analysis , 2014 .

[27]  Ravi Shankar,et al.  Modeling critical success factors of traceability for food logistics system , 2018, Transportation Research Part E: Logistics and Transportation Review.

[28]  Pam Dixon A Failure to “Do No Harm” -- India’s Aadhaar biometric ID program and its inability to protect privacy in relation to measures in Europe and the U.S. , 2017, Health and technology.