Privacy-preserving Distributed Movement Data Aggregation

Monreale A, Wang Wh, 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: LECTURE NOTES IN GEOINFORMATION AND CARTOGRAPHY, pp. 225-245

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