Istituto di Scienza e Tecnologie dell'Informazione     
Monreale A., Wang W. H., Pratesi F., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N. Privacy-preserving distributed movement data aggregation. Danny Vandenbroucke, Bénédicte Bucher, Joep Crompvoets (eds.). (Lecture Notes in Geoinformation and Cartography, vol. 2013). Heidelberg: Springer, 2013.
We propose a novel approach to privacy-preserving 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 people's whereabouts have the potential to reveal intimate personal traits, such as religious or sexual preferences, and may allow 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.
URL: http://link.springer.com/chapter/10.1007%2F978-3-319-00615-4_13
DOI: 10.1007/978-3-319-00615-4_13
Subject Privacy
Distributed systems
K.4.1 Public Policy Issues. Privacy
H.2.8 Database Applications. Spatial databases and GIS

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