%0 Book Section %B Geographic Information Science at the Heart of Europe %D 2013 %T Privacy-Preserving Distributed Movement Data Aggregation %A Anna Monreale %A Hui Wendy Wang %A Francesca Pratesi %A S Rinzivillo %A Dino Pedreschi %A Gennady Andrienko %A Natalia Andrienko %E Vandenbroucke, Danny %E Bucher, Bénédicte %E Crompvoets, Joep %X 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. %B Geographic Information Science at the Heart of Europe %S Lecture Notes in Geoinformation and Cartography %I Springer International Publishing %P 225-245 %@ 978-3-319-00614-7 %U http://dx.doi.org/10.1007/978-3-319-00615-4_13 %R 10.1007/978-3-319-00615-4_13