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Communication Dans Un Congrès Année : 2021

Preventing Attribute and Entity Disclosures: Combining k-anonymity and Anatomy over RDF Graphs

Résumé

In this paper, we are considering the privacy/utility trade-off in privacy-preserving RDF data publishing. Starting from our recent utility-focused work on semantic anatomy, which prevents the disclosure of new information about groups of individuals, we are enriching the framework with the well-established k-anonymity approach. We propose two algorithms which differ on the set of individuals used to perform the k-anonymity. The integration of this privacy method allows us to study the interaction between privacy and utility. Our evaluation emphasizes that the combination of anatomy and k-anonymity preserves the utility qualities of our previous solution and increases the privacy of released data sets.
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Dates et versions

hal-04467983 , version 1 (20-02-2024)

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Citer

Maxime Thouvenot, Olivier Curé, Philippe Calvez. Preventing Attribute and Entity Disclosures: Combining k-anonymity and Anatomy over RDF Graphs. 2021 IEEE International Conference on Big Data (Big Data), Dec 2021, Orlando, United States. pp.5460-5469, ⟨10.1109/BigData52589.2021.9671989⟩. ⟨hal-04467983⟩
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