Evaluating the Risk of Re-Identification in Data Release Strategies: An Attacker-Centric Approach

Simple item page
dc.contributor.authorMesana, Patrick
dc.contributor.authorJutras, Pascal
dc.contributor.authorCrowe, Julien
dc.contributor.authorVial, Gregory
dc.contributor.authorCaporossi, Gilles
dc.date.accessioned2023-12-26T18:36:40Z
dc.date.available2023-12-26T18:36:40Z
dc.date.issued2024-01-03
dc.identifier.doihttps://doi.org/10.24251/HICSS.2024.128
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.othercff24a13-8367-4851-9b98-287584adc1ee
dc.identifier.urihttps://hdl.handle.net/10125/106505
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Science and Machine Learning to Support Business Decisions
dc.subjectadversarial agents
dc.subjectanonymization
dc.subjectdata synthesis
dc.subjectprivacy
dc.subjectrisk of re-identification
dc.titleEvaluating the Risk of Re-Identification in Data Release Strategies: An Attacker-Centric Approach
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractIn this methodological paper, we introduce a novel approach to evaluate the risk of re-identification of individuals associated with data release strategies, including data redaction, data anonymization and data synthesis. More precisely, our approach simulates an attacker performing singling-out attacks as outlined in data protection regulations, and scores attacks based on the linkability of records and the information gain obtained by the attacker. Additionally, we further enhance our approach by simulating attacks as a cooperative game. In this game, the value of the attackers' information resources is determined using Shapley value borrowed from game theory. We also demonstrate the effectiveness of our approach using the Adult Income Census (AIC) dataset before discussing the economical implications associated with a privacy breach. Our work contributes to research and practice on the pressing need to better understand and evaluate the inherent trade-offs that exist between data privacy and utility in organizations.
dcterms.extent10 pages
prism.startingpage1060

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
0103.pdf
Size:
688.2 KB
Format:
Adobe Portable Document Format
Download

Collections

Data Science and Machine Learning to Support Business Decisions