Managing Distributed Queries under Personalized Anonymity Constraints

Axel Michel (1, 2, 3) , Benjamin Nguyen (2, 1, 3) , Philippe Pucheral (2, 1)

1 SMIS - Secured and Mobile Information Systems
2 PETRUS - Personal Trusted cloud
3 SDS - Sécurité des Données et des Systèmes

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. This paper 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. A performance analysis conducted on a real platform shows the effectiveness of the approach.

Mots clés: Secure Hardware; Data Privacy and Security; Big Data; Distributed Query Processing

Domaines: Base de données [cs.DB]

Axel Michel, Benjamin Nguyen, Philippe Pucheral. Managing Distributed Queries under Personalized Anonymity Constraints. 6th International Conference on Data Science, Technology and Applications - DATA 2017, 2017, Madrid, Spain. ⟨hal-01682316⟩