<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Francesca Pratesi</style></author><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%">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></contributors><titles><title><style face="normal" font="default" size="100%">Privacy-Aware Distributed Mobility Data Analytics</style></title><secondary-title><style face="normal" font="default" size="100%">SEBD</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Roccella Jonica</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.
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