Trajectory Privacy Preservation Based on a Fog Structure for Cloud Location Services
The development of mobile cloud computing technology has made location-based service (LBS) increasingly more popular. Given the continuous requests to cloud LBS servers, the amounts of location and trajectory information collected by LBS servers are continuously increasing. Privacy awareness for LBS has been extensively studied in recent years. Among the privacy concerns about LBS, trajectory privacy preservation is particularly important. Based on privacy preservation models, previous work have mainly focused on peer-to-peer and centralized architectures. However, the burden on users is heavy in peerto-peer architectures, because user devices need to communicate with LBS servers directly. In centralized architectures, a trusted third party (TTP) is introduced, and acts as a bridge between users and the LBS server. Anonymity technologies, such as k-anonymity, mix-zone, and dummy technologies, are usually implemented by the TTP to ensure safety. There are certain drawbacks in TTP architectures: Users have no physical control of the TTP. Moreover, the TTP is more attractive to adversaries, because substantially more sensitive information is stored by the TTP. To solve the above-mentioned problems, in this paper, we propose a fog structure to store partial important data with the dummy anonymity technology to ensure physical control, which can be considered as absolutely trust. Compared with cloud computing, fog computing is a promising technique that extends the cloud computing to the edge of a network. Moreover, fog computing provides local computation and storage abilities, wide geo-distribution, and support for mobility. Therefore, mobile usersā?? partial important information can be stored on a fog server to ensure better management. We take the principles of similarity, intersection, practicability, and correlation into consideration and design a dummy rotation algorithm with several properties. The effectiveness of the proposed method is validated through extensive simulations, which show that the proposed method can provide enhanced privacy preservation.
LBS privacy, trajectory privacy preservation, fog computing, rotation.