A New Privacy-Preserving Solution for Clustering Massively Distributed Personal Times-Series
Abstract
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.
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