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• W47444322 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." @default.
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• W47444322 date "2013-01-01" @default.
• W47444322 modified "2023-10-04" @default.
• W47444322 title "Privacy-Preserving Distributed Movement Data Aggregation" @default.
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• W47444322 doi "https://doi.org/10.1007/978-3-319-00615-4_13" @default.
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