Privacy-Preserving Distributed Movement Data Aggregation
| Title | Privacy-Preserving Distributed Movement Data Aggregation |
| Publication Type | Book Chapter |
| Year of Publication | 2013 |
| Authors | Monreale, A, Wang, HWendy, Pratesi, F, Rinzivillo, S, Pedreschi, D, Andrienko, G, Andrienko, N |
| Secondary Authors | Vandenbroucke, D, Bucher, B, Crompvoets, J |
| Book Title | Geographic Information Science at the Heart of Europe |
| Series Title | Lecture Notes in Geoinformation and Cartography |
| Pagination | 225-245 |
| Publisher | Springer International Publishing |
| ISBN Number | 978-3-319-00614-7 |
| 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. |
| URL | http://dx.doi.org/10.1007/978-3-319-00615-4_13 |
| DOI | 10.1007/978-3-319-00615-4_13 |
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