Privacy-preserving location-based traffic density monitoring

Abstract

Traffic density monitoring is an important method to predict road traffic conditions, which can bring some convenience to people's travel in daily life. The common method of traffic density monitoring is to collect and process the location information uploaded by vehicles, but the information of these vehicle location contains a large amount of personal privacy information of vehicle owners, and there is a risk of privacy disclosure. In this paper, we propose a traffic density monitoring system by adding a pseudonym server and a location anonymisation server; the identity information and location information of the vehicles are saved separately. The system can protect both the location privacy of vehicles and the query privacy of users. To prevent dummy locations from being filtered, we calculate the probability distribution of historical location service requests to generate location anonymous sets, which can improve the success rate of anonymity. The location anonymisation server uses the location anonymous set instead of the real location of the vehicle to send to the location-based service provider, which can increase the location privacy security of the vehicle. According to the experimental results of this paper, compared with SimpMaxMinDistds algorithm and MMDS algorithm, our system has better location anonymous set generation efficiency and location privacy protection level.