Privacy-preserving Distributed Movement Data Aggregation

Abstract We tackle the problem of obtaining general information about vehicle traffic in a city from movement data collected by individual vehicles. An important issue here is the possible violation of the privacy of the vehicle users. Movement data are sensitive because they may describe typical movement behaviors and therefore be used for 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, 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.