A Novel Dummy-Based Mechanism to Protect Privacy on Trajectories

In recent years, wireless communication technologies and accurate positioning devices enable us to enjoy various types of location-based services. However, revealing users location information to potentially untrusted LBS providers is one of the most significant privacy threats in location-based services. The dummy-based privacy-preserving approach is a popular technology that can protect real trajectories from exposing to attackers. Moreover, it does not need a trusted third part, while guaranteeing the quality of service. When user requests a service, dummy trajectories anony mize the real trajectory to satisfy privacy-preserving requirements. In this paper, we propose a new privacy model that includes three reasonable privacy metrics. We also design a new algorithm named adaptive dummy trajectories generation algorithm (ADTGA) to derive uniformly distributed dummy trajectories. Dummy trajectories generated by our algorithm can achieve stricter privacy-preserving requirements based on our privacy model. The experimental results show that our proposed algorithm can use fewer dummy trajectories to satisfy the same privacy-preserving requirement than existing algorithms, and the distribution of dummy trajectories is more uniformly.