Differential Privacy in Distributed Mobility Analytics
Movement data are sensitive, because people’s whereabouts may allow re- identification of individuals in a de-identified database and thus can potentially reveal intimate personal traits, such as religious or sexual preferences. In this paper, we focus on a distributed setting in which movement data from individ- ual vehicles are collected and aggregated by a centralized station. We propose a novel approach to privacy-preserving analytical processing within such a dis- tributed setting, and tackle the problem of obtaining aggregated traffic information while preventing privacy leakage from data collection and aggregation. We study and analyze three different solutions based on the differential privacy model and on sketching techniques for efficient data compression. Each solution achieves different trade-off between privacy protection and utility of the transformed data. Using real-life data, we demonstrate the effectiveness of our approaches in terms of data utility preserved by the data transformation, thus bringing empirical evi- dence to the fact that the “privacy-by-design” paradigm in big data analytics has the potential of delivering high data protection combined with high quality even in massively distributed techno-social systems.