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’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.
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Acknowledgments
This work has been partially supported by EU FET-Open project LIFT (FP7-ICT-2009-C n. 255951) and EU FET-Open project DATA SIM (FP7-ICT 270833)
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Monreale, A. et al. (2013). Privacy-Preserving Distributed Movement Data Aggregation. In: Vandenbroucke, D., Bucher, B., Crompvoets, J. (eds) Geographic Information Science at the Heart of Europe. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-00615-4_13
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DOI: https://doi.org/10.1007/978-3-319-00615-4_13
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