Risk Analysis of a GDPR-Compliant Deletion Technique for Consortium Blockchains Based on Pseudonymization

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Conference Lelio Campanile, Pasquale Cantiello, Mauro Iacono, Fiammetta Marulli, Michele Mastroianni — 2021 · Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Venue & metadata

Journal/Proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 12956 LNCS
Pages: 3 – 14
Note: Cited by: 95
Author keywords: Blockchain; GDPR; IoV; Privacy; Pseudonymization; Risk analysis

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.

Keywords

Blockchain Data privacy Risk assessment Block-chain Conflicts of interest GDPR Information recording IoV Privacy Pseudonymization Recording process Traffic management systems Risk analysis

Links & artifacts

DOI Publisher

@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}
}

Suggested citation

Campanile, L., Cantiello, P., Iacono, M., Marulli, F., & Mastroianni, M. (2021). Risk Analysis of a GDPR-Compliant Deletion Technique for Consortium Blockchains Based on Pseudonymization [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12956 LNCS, 3–14. https://doi.org/10.1007/978-3-030-87010-2_1

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