Collaborative-based Trajectory Privacy Preservation Method Under LBS Continuous Queries

With the development of wireless networks and positioning technologies,users who hold mobile devices are at risk of privacy leakage while enjoying high quality location-based services?LBS?.The traditional k-anonymity method can protect users'location privacy in LBS snapshot query to a certain extent,but there is a deficiency in protecting users'privacy under continuous queries.The trajectory privacy problem of mobile users in the case of LBS continuous queries has attracted the attention of many scholars.The trajectory privacy preservation framework under continuous queries can be divided into:centralized structure and distributed structure.The distributed structure can avoid the concentrated attack and performance bottleneck problem faced by the centralized structure.This thesis studies the trajectory privacy preservation problem based on the distributed structure.The main work includes:?1?A collaborative-based trajectory privacy preservation method and an AIRP incentive mechanism are proposed.This method provides location-based services for users through users'collaboration,reducing the frequency of sending real queries to LBSP,prevent LBS server-side attackers from analyzing user data after acquiring user data,and effectively protecting user's trajectory privacy.In response to the selfish behavior of users,this thesis also proposes the AIRP incentive mechanism to encourage users'collaboration by rewarding collaborative users and punish betrayers.Experiments show that the collaborative-based trajectory privacy preservation method with incentive mechanism can effectively disrupt user's trajectory and confuse attacker,thus protecting user's trajectory privacy well.?2?In order to guarantee user's trajectory data availability while protecting user's trajectory privacy,this thesis proposes a6)-anonymity model using rating protocol.Ths model can ensure that target users'trajectory can be indistinguishable from other k users'trajectory during the time period?,effectively defending against correlation attack from attackers.In order to encourage users to collaborate,this thesis designs a socially optimal rating protocol to evaluate the quality of service,gives the optimal recommend strategy and optimal value analysis.The experiment uses the fake query rate and the anonymous rate to evaluate the performance of6)-anonymity model.Experimental results show that our model can efficiently complete the trajectory privacy preservation work.