Section: New Results

Secure Global Computing on Asymmetric Architecture

Participants : Benjamin Nguyen [correspondent] , Philippe Pucheral, Quoc Cuong To.

Asymmetric Architecture Computing: This research direction studies the secure execution of various algorithms on data stored in an unstructured network of Trusted Cells (i.e., personal trusted device) so that each user can keep control over her data. The data could be stored locally in a trusted cell or encrypted on some external cloud. Execution takes place on a specific infrastructure called the Asymmetric Architecture: the network of trusted cells, supported by an untrusted cloud supporting IaaS or PaaS. Our objective is to show that many different algorithms and computing paradigms can be executed on the Asymmetric Architecture, thus achieving secure and private computation. Our first contribution in this area was to study the execution of Privacy Preserving Data Publishing (PPDP) algorithms on such an architecture, and provided generic protocols to deal with all kinds of PPDP algorithms, which are robust against honest-but-curious and malicious adversaries [2] [3] . Our second contribution was to study general SQL queries in this same execution context. We concentrated on the subset of SQL queries without joins, but including Group By and aggregates, and show how to secure their execution in the presence of honest-but-curious attackers [9] . This work was part of Quoc-Cuong To’s Ph.D defended in 2015 [13] . We are extending this general framework through a collaboration with INSA Centre Val de Loire, LIFO Lab and University of Paris Nord, LIPN lab, to study the secure execution of Map/Reduce on the Asymmetric Architecture. Computing MapReduce processes on the Asymmetric Architecture means maintaining the flexibility and efficiency of MapReduce, while adding security into the mix. We have shown in [25] that it is possible to achieve seamless integration of distributed MapReduce processing using trusted cells, while maintaining reasonable performance.

Secure spatio-temporal distributed processing: Mobile participatory sensing could be used in many applications such as vehicular traffic monitoring, pollution tracking, or even health surveying (e.g., to allow measuring in real-time the individual exposure to environmental risk factors or the propagation of an epidemic). However, its success depends on finding a solution for querying a large number of users which protects user location privacy and works in real-time. We addressed these issues and proposed PAMPAS, a privacy-aware mobile distributed system for efficient data aggregation in mobile participatory sensing. In PAMPAS, mobile devices enhanced with secure hardware, called secure probes, perform distributed query processing, while preventing users from accessing other users' data. Secure probes exchange data in encrypted form with help from an untrusted supporting server infrastructure. PAMPAS uses two efficient, parallel, and privacy-aware protocols for location-based aggregation and adaptive spatial partitioning of secure probes. Our experimental results and security analysis demonstrate that these protocols are able to collect, aggregate and share statistics or derived data in real-time, without any privacy leakage. This work is part of Dai Hai Ton That’s Ph.D. thesis, co-supervised by Iulian Sandu Popa. The system implementation was demonstrated in [26] , and a paper describes the technical details of the system [31] .