A Game Theory-Based Analysis of Data Privacy in Vehicular Sensor Networks

Mobile traces, collected by vehicular sensor networks (VSNs), facilitate various business applications and services. However, the traces can be used to trace and identify drivers or passengers, which raise significant privacy concerns. Existing privacy protecting techniques may not be suitable, due to their inadequate considerations for the data accuracy requirements of different applications and the adversary's knowledge and strategies. In this paper, we analyze data privacy issues in VSNs with a game theoretic model, where a defender uses the privacy protecting techniques against the attack strategies implemented by an adversary. We study both the passive and active attack scenarios, and in each scenario we consider the effect of different data accuracy requirements on the performance of defense measures. Through the analysis results on real-world traffic data, we show that more inserted bogus traces or deleted recorded samples show a better performance when the cost of defense measures is small, whereas doing nothing becomes the best strategy when the cost of defense measures is very large. In addition, we present the optimal defense strategy that provides the defender with the maximum utility when the adversary implements the optimal attack strategy.