Monreale, AnnaAnnaMonrealeWang, W.H.W.H.WangPratesi, FrancescaFrancescaPratesiRinzivillo, SalvatoreSalvatoreRinzivilloPedreschi, DinoDinoPedreschiAndrienko, GennadyGennadyAndrienkoAndrienko, NataliaNataliaAndrienko2022-03-052022-03-052013https://publica.fraunhofer.de/handle/publica/24839410.1007/978-3-319-00615-4_132-s2.0-84939633427We 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.en005book article