Risk Analysis of a GDPR-Compliant Deletion Technique for Consortium Blockchains Based on Pseudonymization
Published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021
Recommended citation: Lelio Campanile, Pasquale Cantiello, Mauro Iacono, Fiammetta Marulli, Michele Mastroianni, "Risk Analysis of a GDPR-Compliant Deletion Technique for Consortium Blockchains Based on Pseudonymization." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115698960&doi=10.1007%2f978-3-030-87010-2_1&partnerID=40&md5=445c0e92ccab7722cfb6aa1c79f1802f
Cited by: 95
Abstract: Blockchains provide a valid and profitable support for the implementation of trustable and secure distributed ledgers, in support to groups of subjects that are potentially competitors in conflict of interest but need to share progressive information recording processes. Blockchains prevent data stored in blocks from being altered or deleted, but there are situations in which stored information must be deleted or made inaccessible on request or periodically, such as the ones in which GDPR is applicable. In this paper we present literature solutions and design an implementation in the context of a traffic management system for the Internet of Vehicles based on the Pseudonymization/Cryptography solution, evaluating its viability, its GDPR compliance and its level of risk. © 2021, Springer Nature Switzerland AG.
Author Keywords: Blockchain; GDPR; IoV; Privacy; Pseudonymization; Risk analysis
Bibtex citation:
@ARTICLE{Campanile20213,
author = "Campanile, Lelio and Cantiello, Pasquale and Iacono, Mauro and Marulli, Fiammetta and Mastroianni, Michele",
title = "Risk Analysis of a GDPR-Compliant Deletion Technique for Consortium Blockchains Based on Pseudonymization",
year = "2021",
journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
volume = "12956 LNCS",
pages = "3 – 14",
doi = "10.1007/978-3-030-87010-2\_1",
type = "Conference paper"
}