Practical Fully-Decentralized Secure Aggregation for Personal Data Management Systems

Abstract

Personal Data Management Systems (PDMS) are flourishing, boosted by legal and technical means like smart disclosure, data portability and data altruism. A PDMS allows its owner to easily collect, store and manage data, directly generated by her devices, or resulting from her interactions with companies or administrations. PDMSs unlock innovative usages by crossing multiple data sources from one or many users, thus requiring aggregation primitives. Indeed, aggregation primitives are essential to compute statistics on user data, but are also a fundamental building block for machine learning algorithms. This paper proposes a protocol allowing for secure aggregation in a massively distributed PDMS environment, which adapts to selective participation and PDMSs characteristics, and is reliable with respect to failures, with no compromise on accuracy. Preliminary experiments show the effectiveness of our protocol which can adapt to several contexts with varying PDMSs characteristics in terms of communication speed or CPU resources and can adjust the aggregation strategy to the estimated selective participation. CCS CONCEPTS• Computer systems organization → Architectures; • Information systems → Data management systems.

Highly distributed and privacy-preserving queries on personal data management systems

“…However, for classical aggregate functions, more secure distributed aggregation protocols, adapted to the DIS-PERS architecture can be envisioned. This is the focus of our future work extending DISPERS (preliminary results in [45]). Improving the DHT overlay network latency.…”

“…Fortunately, local results alone do not bring many insights to the attacker, especially if lq can be restricted in some ways (e.g., lq should not return identifying results). Considering working on ciphertext is part of our future work (see preliminary results in [45]). Full association leakage.…”