Abstract: By using crowdsourcing, vehicular sensor networks (VSN) are considered essential for achieving automatic and dynamic traffic information collection, and have created various fresh new business applications and services in our daily lives. However, the published trajectories that collected in VSNs raise significant privacy concerns. These existing methods, such as anonymization and cloaking techniques, though they are attractive for providing strong privacy guarantees, generally fail to satisfy the accuracy requirements of the trajectory data based applications. In addition, different attack strategies will result in quite different performance under various privacy preserving strategies. In order to address these challenges, we present a location privacy protection method, the DefenseGame algorithm. Given a set of trajectories and a probability density function for side information, the algorithm can assist the defender in selecting the optimal defense strategies by calculating the equilibriums in attack and defense games. In the attack and defense game, we use a game-theoretic model to capture the behavior of the adversary and defender, and we show the effectiveness of the two kinds of defense strategies in the adversary’s inference attacks. Our experimental results indicate that the same defense strategy shows different performance for attack strategies and the proposed algorithm can help to obtain higher defender’s utility compared with other approaches.