Analyzing and Preventing Data Privacy Leakage in Connected Vehicle Services 2019-01-0478
The rapid development of connected and automated vehicle technologies together with cloud-based mobility services are revolutionizing the transportation industry. As a result, huge amounts of data are being generated, collected, and utilized, hence providing tremendous business opportunities. However, this big data poses serious challenges mainly in terms of data privacy. The risks of privacy leakage are amplified by the information sharing nature of emerging mobility services and the recent advances in data analytics. In this paper, we first provide an overview of the connected vehicle landscape and point out potential privacy threats. We demonstrate two of the risks, namely additional individual information inference and user de-anonymization, through concrete attack designs. Our experiments on real-world datasets show that the individual information inference and user de-anonymization attacks are feasible in corresponding scenarios. We also propose corresponding countermeasures to defend against such privacy attacks and consider maintaining data usability at the same time. We evaluate the feasibility of our defense strategies using real-world vehicular data.
Huaxin Li, Di Ma, Brahim Medjahed, Yu Seung Kim, Pramita Mitra
University of Michigan, Ford Motor Co., Ltd.