The advent of on-body/at-home sensors connected to personal devices leads to the generation of fine grain highly sensitive personal data at an unprecendent rate. However, despite the promises of large scale analytics there are obvious privacy concerns that prevent individuals to share their personnal data. In this paper, we propose Chiaroscuro, a complete solution for clustering personal data with strong privacy guarantees. The execution sequence produced by Chiaroscuro is massively distributed on personal devices, coping with arbitrary connections and disconnections. Chiaroscuro builds on our novel data structure, called Diptych, which allows the participating devices to collaborate privately by combining encryption with differential privacy. Our solution yields a high clustering quality while minimizing the impact of the differentially private perturbation. Chiaroscuro is both correct and secure. Finally, we provide an experimental validation of our approach on both real and synthetic sets of time-series.
Allard et al. REF proposed Chiaroscuro, a solution for clustering personal time-series that are distributed on personal devices with privacy guarantees.
Chiaroscuro: Transparency and Privacy for Massive Personal Time-Series Clustering
SIGMOD '15
Computer Science