Research And Implementation Of Location Privacy Protection Technologies Of Spatio-Temporal Objects

With development of mobile positioning technology, location privacy protection of spatio-temporal objects in LBS(Location-based Service) is becoming a research hot spot.At present, the study of location privacy protection of spatio-temporal objects has many problems in context-aware location privacy protection, location anonimity and query process and so on. This paper mainly focuses on context-aware location privacy protection, trajectory anonimity of spatio-temporal objects, query process, the main contributions are as follows:Firstly, on the basis of discussing the exist methods of context-aware location privacy protection and analysising the drawback of the current methods, it designs an architecture NHCPP for context-aware location privay protection. It introduces the context-aware module in NHCPP in detail, then privides the relavent context-aware privacy policy primitive and privacy policy. Finally, it presents the process flow of NHCPP through instances.Secondly, it provides a trajectory privacy protection algorithm PNWA to meet personalized privacy requirements of spatio-temporal objects. This algorithm is an improvement of the algorithm NWA. In view of spatio-temporal objects may have different privacy requirements under different circustances. Algorithm PNWA allows spatio-temporal objects to submit personalized privacy requirements under different circustances, solving the problem that triditional algorithm has the same privacy requirements and also improving the trajectory anonimity success rate. Finally, experiments show that anonimty success rate is improved after processing trajectory data set with algorithm PNWA .Thirdly, it provides process methods for typical queries in range query and aggregate query, then proposes the algorithm PubQ_Over_PriD for public query over privacy data in nearest neighbor query and presents the process steps of algorithm PubQ_Over_PriD. After data set being processed by algorithm PubQ_Over_PriD, target objects which can not be the query result are removed, effectively reducing the size of candidate result set. Finally, experiments show that after orginal data set being processed by algorithm PubQ_Over_PriD, the amount of target objects in candidate set is reduced greatly.