Document - Privacy-preserving Distributed Movement Data Aggregation

2013

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Privacy-preserving Distributed Movement Data Aggregation

Monreale A., Wang W. H., Pratesi F., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N.

Privacy Distributed systems Mobility

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.

Source: Geographic Information Science at the Heart of Europe, edited by Danny Vandenbroucke, Bénédicte Bucher, Joep Crompvoets, pp. 225–245, 2013

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@inbook{oai:it.cnr:prodotti:277834,
	title = {Privacy-preserving Distributed Movement Data Aggregation},
	author = {Monreale A. and Wang W. H. and Pratesi F. and Rinzivillo S. and Pedreschi D. and Andrienko G. and Andrienko N.},
	doi = {10.1007/978-3-319-00615-4_13},
	booktitle = {Geographic Information Science at the Heart of Europe, edited by Danny Vandenbroucke, Bénédicte Bucher, Joep Crompvoets, pp. 225–245, 2013},
	year = {2013}
}