A fast privacy-preserving framework for continuous location-based queries in road networks

The prevalence of location based services (LBS) gives rise to personal privacy concerns as users share their locations and queries to obtain desired services. For continuous queries where users report their locations periodically, attackers can infer more about users' privacy by analyzing the correlations of their snapshot samples. Traditional privacy-preserving solutions designed in Euclidean space can be hardly applied to the road network environment because of their ignorance of network topological properties. In this paper, we propose a novel continuous query privacy-preserving framework in road networks. Our framework is based on the concepts of k-anonymity and l-diversity. To achieve the quality of service, the distance limitation is taken into account. We build an Snet hierarchy based on the density of users, history traces, and road network topologies to accelerate the cloaking process performed at the anonymization server. Two types of cloaking algorithms, for a single user and a batch of users, are designed. The security analysis shows that our framework is robust to typical attacks. We evaluate our framework from the aspects of privacy-preserving ability, quality of service, and system performance, which indicates that our framework can provide good privacy protection while ensuring users' quality of