2016 IEEE 32nd International Conference on Data Engineering (ICDE) (2016)
May 16, 2016 to May 20, 2016
Tristan Allard , IRISA & Univ. Rennes 1, 263 av. Général de Gaulle, 35042 cedex, France
Georges Hebrail , EDF R&D, 1 av. Général de Gaulle, BP 408, 92141 Clamart cedex, France
Florent Masseglia , Inria & Lirmm, Univ. Montpellier, Campus St Priest, 860 rue de St Priest, 34095 cedex 5, France
Esther Pacitti , Inria & Lirmm, Univ. Montpellier, Campus St Priest, 860 rue de St Priest, 34095 cedex 5, France
New personal data fields are currently emerging due to the proliferation of on-body/at-home sensors connected to personal devices. However, strong privacy concerns prevent individuals to benefit from large-scale analytics that could be performed on this fine-grain highly sensitive wealth of data. We propose a demonstration of Chiaroscuro, a complete solution for clustering massively-distributed sensitive personal data while guaranteeing their privacy. The demonstration scenario highlights the affordability of the privacy vs. quality and privacy vs. performance tradeoffs by dissecting the inner working of Chiaroscuro - launched over energy consumption times-series -, by exposing the results obtained by the individuals participating in the clustering process, and by illustrating possible uses.
Privacy, Encryption, Graphical user interfaces, Convergence, Aggregates, Data privacy
T. Allard, G. Hebrail, F. Masseglia and E. Pacitti, "A new privacy-preserving solution for clustering massively distributed personal times-series," 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland, 2016, pp. 1370-1373.