LAUSR

LBSs have been prospering over recent years. Although many LPPMs are proposed to solve specific problems under certain settings, it is still hard to satisfy the different demands of users due to the diverse requirements for quality of service (QoS) and privacy preservation. In this article, we propose an adaptive scheme to achieve fine-grained QoS for privacy-aware users in LBSs. Specifically, we construct a Bayesian-based classification model to identify the QoS requirements of users under different circumstances. QoS and privacy degrees provided by LBSs are carefully defined based on the concept of rate-distortion theory, which plays an important role in bridging the LPPMs with users' requirements. When users request the location service from the built-in GPS module in smart devices, we first specify their circumstances and requirements, then reply with the modified locations. We also design an efficient algorithm to dynamically choose the appropriate privacy strategy according to the QoS requirements of users in different circumstances. Experimental results show that our scheme can identify QoS requirements accurately and protect location privacy adaptively.