%0 Conference Paper %B SEBD %D 2013 %T Privacy-Aware Distributed Mobility Data Analytics %A Francesca Pratesi %A Anna Monreale %A Hui Wendy Wang %A S Rinzivillo %A Dino Pedreschi %A Gennady Andrienko %A Natalia Andrienko %X 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. %B SEBD %C Roccella Jonica %G eng