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Privacy-aware distributed mobility data analytics

: Pratesi, F.; Monreale, A.; Wang, H.; Rinzivillo, S.; Pedreschi, D.; Andrienko, G.; Andrienko, N.

Buccafurri, F.:
21st Italian Symposium on Advanced Database Systems, SEBD 2013 : Roccella Jonica, Reggio Calabria, Italy, 30 June - 4 July 2013
Red Hook, NY: Curran, 2014
ISBN: 978-1-62993-949-0
Italian Symposium on Advanced Database Systems (SEBD) <21, 2013, Roccella Jonica>
Fraunhofer IAIS ()

We propose an approach to preserve privacy in an analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because they may describe typical movement behaviors and therefore be used for re-identification of individuals in a database.We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.