@inbook {571,
	title = {Privacy-Preserving Distributed Movement Data Aggregation},
	booktitle = {Geographic Information Science at the Heart of Europe},
	series = {Lecture Notes in Geoinformation and Cartography},
	year = {2013},
	pages = {225-245},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {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{\textquoteright}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.},
	isbn = {978-3-319-00614-7},
	doi = {10.1007/978-3-319-00615-4_13},
	url = {http://dx.doi.org/10.1007/978-3-319-00615-4_13},
	author = {Anna Monreale and Hui Wendy Wang and Francesca Pratesi and S Rinzivillo and Dino Pedreschi and Gennady Andrienko and Natalia Andrienko},
	editor = {Vandenbroucke, Danny and Bucher, B{\'e}n{\'e}dicte and Crompvoets, Joep}
}
