Private and Scalable Execution of SQL Aggregates on a Secure Decentralized Architecture

Cuong Quoc To 1, 2 Benjamin Nguyen 2, 3 Philippe Pucheral 1, 2
2 SMIS - Secured and Mobile Information Systems
CNRS - Centre National de la Recherche Scientifique : 8144, PRISM - Parallélisme, Réseaux, Systèmes, Modélisation, Inria Saclay - Ile de France
3 SDS - Sécurité des Données et des Systèmes
LIFO - Laboratoire d'Informatique Fondamentale d'Orléans
Abstract : Current applications, from complex sensor systems (e.g. quantified self) to online e-markets acquire vast quantities of personal information which usually end-up on central servers where they are exposed to prying eyes. Conversely, decentralized architectures helping individuals keep full control of their data, complexify global treatments and queries, impeding the development of innovative services. This paper precisely aims at reconciling individual's privacy on one side and global benefits for the community and business perspectives on the other side. It promotes the idea of pushing the security to secure hardware devices controlling the data at the place of their acquisition. Thanks to these tangible physical elements of trust, secure distributed querying protocols can reestablish the capacity to perform global computations, such as SQL aggregates, without revealing any sensitive information to central servers. This paper studies how to secure the execution of such queries in the presence of honest-but-curious and malicious attackers. It also discusses how the resulting querying protocols can be integrated in a concrete decentralized architecture. Cost models and experiments on SQL/AA, our distributed prototype running on real tamper-resistant hardware, demonstrate that this approach can scale to nationwide applications.
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Submitted on : Monday, April 4, 2016 - 10:12:31 AM
Last modification on : Thursday, March 14, 2019 - 9:58:07 AM
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Cuong Quoc To, Benjamin Nguyen, Philippe Pucheral. Private and Scalable Execution of SQL Aggregates on a Secure Decentralized Architecture. ACM Transactions on Database Systems, Association for Computing Machinery, 2016, 41 (3), pp.46. ⟨hal-01296432⟩

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