DALP: A Demand-aware Location Privacy In Continuous Location-based Service

With the development of wireless communication and mobile positioningtechnology, the Location-based Service (LBS) on mobile handled devices has beenwidely used. At the same time, location privacy has been a serious concern for mobileusers who use LBS provided by the third-party provider via mobile network. A lot ofanonymity algorithm has been designed to protect the privacy of the mobile users. Butmost of the existing works concerning the continuous LBS queries just focus on users’privacy demands with little consideration of the service of quality (QoS), leading tooversized cloaking regions.In this paper, we propose a Demand-Aware Location Protection model DALP forcontinuous LBS query. In summary, our main contributions are as follows:(1) The demands-aware location privacy protection model DALP is proposed forthe continuous LBS query, allowing a user to set his personalized privacy and QoSrequirements for different LBS queries.(2) User’s personalized privacy and QoS requirements perhaps result that in somequery points the privacy and QoS requirement cannot be met together and the locationprivacy protection cannot be provided for the continuous LBS query. We point out itsunderlying reason is that in few LBS query regions the footprints are rare or the privacyrequirements are set over high. Then, a maximum Demands-aware Request Sequencealgorithm DAQS is proposed. Through finding and restraining the queries in thoseregions, most of LBS queries are satisfied, thus, the longest LBS query sequence isobtained which can satisfy a user’s specific privacy and QoS requirements.(3) On the premise that the user’s privacy requirement is met, we propose twoalgorithms to further shrink the cloaking regions. Through deleting the farthestfootprints and diminishing the perimeters of cloaking regions respectively, the cloakingregions formed based on the common user historical footprints are minimized, reducingthe query latency and the server’s workload and providing better service quality forusers.We use the network-based generator of moving objects simulator to simulate theactual application scene. Then, the extensive simulations on a large dataset prove theeffectiveness and efficiency of our DALP model under various location privacy andQoS demands. This DALP model can achieve both high success rate and high-quality location anonymity while satisfying the demands of both the privacy and QoS.