01685nas a2200241   4500008003900000020002200039245006100061210006000122260003800182300001200220520094300232100001901175700002101194700002301215700001801238700002001256700002301276700002301299700002501322700002401347700002101371856005101392       2013                          d  a978-3-319-00614-700aPrivacy-Preserving Distributed Movement Data Aggregation0 aPrivacyPreserving Distributed Movement Data Aggregation  bSpringer International Publishing  a225-2453 aWe 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.1 aMonreale, Anna1 aWang, Hui, Wendy1 aPratesi, Francesca1 aRinzivillo, S1 aPedreschi, Dino1 aAndrienko, Gennady1 aAndrienko, Natalia1 aVandenbroucke, Danny1 aBucher, Bénédicte1 aCrompvoets, Joep  uhttp://dx.doi.org/10.1007/978-3-319-00615-4_13