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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.


Vandenbroucke, D.:
Geographic information science at the heart of Europe
Heidelberg: Springer, 2013 (Lecture notes in geoinformation and cartography)
ISBN: 978-3-319-00614-7
ISBN: 978-3-319-00615-4
Aufsatz in Buch
Fraunhofer IAIS ()

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 peopleâs 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.