Personalizing Privacy Constraints in Generalization-based Anonymization Models

Axel Michel 1, 2
1 SMIS - Secured and Mobile Information Systems
PRISM - Parallélisme, Réseaux, Systèmes, Modélisation, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : 8144
2 SDS - Sécurité des Données et des Systèmes
LIFO - Laboratoire d'Informatique Fondamentale d'Orléans
Abstract : Summary : The benefit of performing Big data computations over individual’s microdata is manifold, in the medical, energy or transportation fields to cite only a few, and this interest is growing with the emergence of smart-disclosure initiatives around the world.However, these computations often expose microdata to privacy leakages, explaining the reluctance of individuals to participate in studies despite the privacy guarantees promised by statistical institutes. To regain indivuals’trust, it becomes essential to propose user empowerment solutions, that is to say allowing individuals to control the privacy parameter used to mke computations over their microdata. This work proposes a novel concept of personalized anonymisation based on data generalization and user empowerment. Firstly, this manuscript proposes a novel approach to push personalized privacy guarantees in the processing of database queries so that individuals can disclose different amounts of information (i.e. data at different levels of accuracy) depending on their own perception of the risk. Moreover, we propose a decentralized computing infrastructure based on secure hardware enforcing these personalized privacy guarantees all along the query execution process. Secondly, this manuscript studies the personalization of anonymity guarantees when publishing data. We propose the adapation of existing heuristics and a new approach based on constraint programming. Experiments have been done to show the impact of such personalization on the data quality. Individuals’privacy constraints have been built and realistically using social statistic studies.
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Axel Michel. Personalizing Privacy Constraints in Generalization-based Anonymization Models. Cryptography and Security [cs.CR]. INSA Centre Val de Loire, 2019. English. ⟨tel-02269565⟩

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