A Location Privacy and Query Privacy Joint Protection Scheme for POI Query in Vehicular Networks

In vehicular networks, the Point of Interest (POI) query was widely used in Location-Based Services (LBS) for vehicle applications. However, since the attackers can easily access the location, query content, and other information, it is difficult to protect the LBS privacy of vehicle users only using location privacy protection or query privacy protection independently. Therefore, we propose a location privacy and query privacy joint protection scheme based on dummy sequences. According to the limitations of the POI query, we model the correlations between location privacy and query privacy into semantic correlation, temporal correlation, and spatial-temporal attribute correlation, characterized by the Euclidean distance and the association rule algorithm. Moreover, we formulate the dummy sequence selection as a constrained multi-objective optimization problem to obtain the query sequence with a high level of anonymity and a big cloaking region. Experimental results demonstrate that our scheme can resist joint attacks on location privacy and query privacy and protect users' LBS privacy more efficiently compared to the existing schemes.