%0 Journal Article
%T A Game Theory-Based Analysis of Data Privacy in Vehicular Sensor Networks
%A Yunhua He
%A Limin Sun
%A Weidong Yang
%A Hong Li
%J International Journal of Distributed Sensor Networks
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/838391
%X 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. 1. Introduction With the advances and wide adoption of wireless communication technologies, vehicles are now often equipped with wireless devices that allow them to communicate with each other (V2V) as well as with roadside infrastructures (V2I). The V2V and V2I communications make driving more safe and improve a driver¡¯s driving experiences. Such communication networks are called Vehicular Ad Hoc Networks (VANETs). However, with the increasing needs for sensing and data acquisition in cities, VANETs have turned into Vehicular Sensor Networks (VSNs) [1]. VSNs exploit vehicles and passengers to capture the occurrence of events, such as traffic volume, road surface condition, chemical, and radiation. The location traces in the traffic-related data create various fresh new business applications and services, such as map drawing [2], traffic prediction [3], city planning, and mobile network analysis [4]. However, the places in these location traces that a driver or passenger has visited may reveal his/her sensitive information, such as traffic law violations, political affiliations, and medical conditions [5, 6]. Although the information about vehicular mobility traces are often collected in an anonymous way, an adversary can reidentify the true owner of a trace. Because the location information of drivers
%U http://www.hindawi.com/journals/ijdsn/2014/838391/