<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anna Monreale</style></author><author><style face="normal" font="default" size="100%">Hui Wendy Wang</style></author><author><style face="normal" font="default" size="100%">Francesca Pratesi</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Gennady Andrienko</style></author><author><style face="normal" font="default" size="100%">Natalia Andrienko</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Vandenbroucke, Danny</style></author><author><style face="normal" font="default" size="100%">Bucher, Bénédicte</style></author><author><style face="normal" font="default" size="100%">Crompvoets, Joep</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Privacy-Preserving Distributed Movement Data Aggregation</style></title><secondary-title><style face="normal" font="default" size="100%">Geographic Information Science at the Heart of Europe</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Geoinformation and Cartography</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-00615-4_13</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pages><style face="normal" font="default" size="100%">225-245</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-00614-7</style></isbn><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>