PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Pratesi F., Monreale A., Wang H., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N. Privacy-aware distributed mobility data analytics. In: SEBD 2013 - 21st Italian Symposium on Advanced Database Systems (Roccella Jonica, Reggio Calabria, Italy, 30 June - 3 July 2013). Atti, pp. 329 - 342. UniversitÓ di Reggio Calabria, 2013.
 
 
Abstract
(English)
We propose an approach to preserve privacy in an analytical process- ing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by in- dividual 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 pri- vacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the ef- fectiveness of our approach also in terms of data utility preserved by the data transformation.
Subject Privacy
Distributed systems
Mobility
K.4.1 Public Policy Issues. Privacy
H.2.8 Database Applications. Spatial databases and GIS


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