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Open Computer Science

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Privacy-aware blockchain for personal data sharing and tracking

Md Mehedi Hassan Onik / Chul-Soo Kim / Nam-Yong Lee / Jinhong Yang
Published Online: 2019-04-15 | DOI: https://doi.org/10.1515/comp-2019-0005

Abstract

Secure data distribution is critical for data accountability. Surveillance caused privacy breaching incidents have already questioned existing personal data collection techniques. Organizations assemble a huge amount of personally identifiable information (PII) for data-driven market analysis and prediction. However, the limitation of data tracking tools restricts the detection of exact data breaching points. Blockchain technology, an ‘immutable’ distributed ledger, can be leveraged to establish a transparent data auditing platform. However, Art. 42 and Art. 25 of general data protection regulation (GDPR) demands ‘right to forget’ and ‘right to erase’ of personal information, which goes against the immutability of blockchain technology. This paper proposes a GDPR complied decentralized and trusted PII sharing and tracking scheme. Proposed blockchain based personally identifiable information management system (BcPIIMS) demonstrates data movement among GDPR entities (user, controller and processor). Considering GDPR limitations, BcPIIMS used off-the-chain data storing architecture. A prototype was created to validate the proposed architecture using multichain. The use of off-the-chain storage reduces individual block size. Additionally, private blockchain also limits personal data leaking by collecting fast approval from restricted peers. This study presents personal data sharing, deleting, modifying and tracking features to verify the privacy of proposed blockchain based personally identifiable information management system.

Keywords: general data protection regulation; GDPR; personally identifiable information; PII; privacy policy; distributed ledger; off-chain data; identity management; personal information tracking

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About the article

Received: 2018-11-30

Accepted: 2019-03-04

Published Online: 2019-04-15


Citation Information: Open Computer Science, Volume 9, Issue 1, Pages 80–91, ISSN (Online) 2299-1093, DOI: https://doi.org/10.1515/comp-2019-0005.

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© 2019 Md Mehedi Hassan Onik et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution 4.0 Public License. BY 4.0

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