Location Privacy Preservation Based on Continuous Queries for Location-Based Services

In recent years, location-based services (LBSs) have become increasingly popular, as they can greatly facilitate people’s daily life. However, LBSs also raise serious concerns that the collected big data can be utilized by adversaries to track users’ movements, and infer their specific locations and living habits. To address this privacy issue, we propose an algorithm based on the k-anonymity criterion, to generate dummy locations to protect users’ privacy. We also develop a new metric, the trajectory entropy, to measure the performance of anonymity. Different from the existing technologies, which focus on the stationary location privacy, we further consider the correlation between adjacent locations in the continuous queried process. We assume it as a Markov process and exploit such Markov process to achieve privacy protection. Our simulation results on the real-life dataset show that compared with other algorithms, the proposed algorithm exhibits superior performance in providing location anonymity by resisting most attacks.