Research On Location Privacy Protection On Mobile Users Over Road Networks

With the rapid development of mobile intelligent terminal technology and communication equipment,Location based services(LBS)are widely used.While LBS brings a lot,personal privacy leakages are also highlighted.Most of existing location cloaking approaches over road networks are only applicable for snapshots,ignoring user mobility,which could easily lead to hidden location reasoning attacks and continuous query location privacy disclosure.This paper studies the location privacy protection on mobile users over road network.The major works of this paper include:Based on the background knowledge of mobile users,this paper studies the hidden location inference attack of mobile users.Two kinds of hidden location inference attack models are proposed,which consider the background knowledge of mobile users,such as the location sequential,time influence,user association,service category preference and category popularity of point of interest etc..Based on these models,a hidden location inference algorithm are also proposed.Finally,we evaluate the accuracy of the model and verify the effectiveness of the algorithm by using two real data sets.Due to continuous location frequently updated of mobile users,which could easily lead to continuous query privacy disclosure.We observe that mobile objects have similar spatial and temporal features due to the existing of network topology.In order to resist continuous query attacks and location-dependent attacks simultaneously,we propose a continuous queries privacy protection algorithm based on spatial-temporal similarity over road networks.The algorithm adopt user grouping and K-sharing privacy requirement strategies to constitute cloaking user sets,which is used to resist continuous queries attack.Then,with the same premise of cloaking user sets,a continuous cloaking segment sets generating algorithm is proposed to resist location-dependent attacks,which makes a balance between location privacy and service quality.Finally,we conduct series of experiments to verify our algorithm with six evaluation measures.The main conclusions are drawn and the interesting points for future work are provided in the end of dissertation.