An Efficient Privacy-Preserving Scheme for Traffic Monitoring Services in Vehicular Networks

Chen Gu, Xuande Cui, Donghui Hu

Abstract: Traffic monitoring services show great potential for improving the traffic efficiency in vehicular networks. Drivers can obtain the latest information on their upcoming routes by sending location-based queries to the traffic monitoring server. However, it is inefficient to query all events on the route due to the timeliness of traffic events. Besides, sending location-based queries will expose drivers’ privacy. Existing research does not consider both issues under the traffic monitoring scenario. In this paper, we propose an efficient privacy-preserving scheme that ensures privacy is protected in two dimensions. Both location privacy and identity privacy are preserved, such that attackers cannot observe the real location as well as the identity via continuous queries. Specifically, the scheme first segments the whole route into multiple ones. Then drivers send endpoints in each segmented route to satisfy the timeliness requirement. We propose a location obfuscation mechanism based on geo-indistinguishability and utilize it on every segmented route. We address an issue in geo-indistinguishability where the obfuscated location is unreasonable. Additionally, continuous queries may expose the driver’s identity. We thus define a type of attack called identity linking attack and propose two possible solutions. We finally conduct experiments on the real dataset. Experimental results demonstrate the efficiency of our proposed scheme.