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

Tristan Allard 1 Georges Hébrail 2 Florent Masseglia 3 Esther Pacitti 3
1 DRUID - Declarative & Reliable management of Uncertain, user-generated Interlinked Data
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
2 EDF R&D - EDF Division Recherche et Développement [Clamart]
3 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
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.
Keywords : time-series differential privacy gossip k-means
Type de document :
Communication dans un congrès
ICDE: International Conference on Data Engineering, May 2016, Helsinki, Finland. 32nd IEEE International Conference on Data Engineering, ICDE 2016, 2016, <http://icde2016.fi/>
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Contributeur : Florent Masseglia <>
Soumis le : mercredi 2 novembre 2016 - 14:52:23
Dernière modification le : mardi 11 juillet 2017 - 09:48:16
Document(s) archivé(s) le : vendredi 3 février 2017 - 14:17:31

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Tristan Allard, Georges Hébrail, Florent Masseglia, Esther Pacitti. A New Privacy-Preserving Solution for Clustering Massively Distributed Personal Times-Series. ICDE: International Conference on Data Engineering, May 2016, Helsinki, Finland. 32nd IEEE International Conference on Data Engineering, ICDE 2016, 2016, <http://icde2016.fi/>. <lirmm-01270268>

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