A Novel Time-Obfuscated Algorithm for Trajectory Privacy Protection

Location-based services (LBS) which bring so much convenience to our daily life have been intensively studied over the years. Generally, an LBS query processing can be categorized into snapshot and continuous queries which access user location information and return search results to the users. An LBS has full control of the location information, causing user privacy concerns. If an LBS provider has a malicious intention to breach the user privacy by tracking the users' routes to their destinations, it incurs a serious threat. Most existing techniques have addressed privacy protection mainly for snapshot queries. However, providing privacy protection for continuous queries is of importance, since a malicious LBS can easily obtain complete user privacy information by observing a sequence of successive query requests. In this paper, we propose a comprehensive trajectory privacy technique and combine ambient conditions to cloak location information based on the user privacy profile to avoid a malicious LBS reconstructing a user trajectory. We first propose an r-anonymity mechanism which preprocesses a set of similar trajectories R to blur the actual trajectory of a service user. We then combine k-anonymity with s road segments to protect the user's privacy. We introduce a novel time-obfuscated technique which breaks the sequence of the query issuing time for a service user to confuse the LBS so it does not know the user trajectory, by sending a query randomly from a set of locations residing at the different trajectories in R. Despite the randomness incurred from the obfuscation process for providing strong trajectory privacy protection, the experimental results show that our trajectory privacy technique maintains the correctness of the query results at a competitive computational cost.