Movement data are sensitive, because people’s whereabouts may allow re-
identification of individuals in a de-identified database and thus can poten-
tially reveal intimate personal traits, such as religious or sexual preferences.
In this thesis, we focus on a distributed setting in which movement data from individual vehicles are collected and aggregated by a centralized station.
We propose a novel approach to privacy-preserving analytical processing within such a distributed 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 a 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 evidence to the fact that the privacy-by-design paradigm in big data analysis has the potential of delivering high data protection combined with high quality even in massively distributed techno-social systems
