Research On Location Privacy Preserving Method Based On K-anonymity

With the rapid development of wireless communication technologies and mobile location technologies,location-based services have been widely used.That requires the user to provide accurate location information.However,the users also face the risk of location privacy disclosure while enjoying convenience.The leakage of location privacy has become an important factor constraining the development of location-based services.Therefore,location privacy problems have received widely attention from domestic and foreign scholar.This thesis mainly focuses on location privacy problems based on location services and systematically analyzes existing location privacy-preserving technologies.Aiming at the trusted third-party central server performance bottleneck and the large k-anonymity area in space filling curve schemes,Geohash-based location privacy-preserving algorithm and location-based privacy-preserving algorithm based on string matching are proposed.The main work is as follows:1)A location privacy-preserving algorithm based on Geohash is proposed to solve the trusted third-party central server bottleneck performance problem.The Geohash coding is introduced to reduce the dimension of the position information and perform string fuzzy query to determine the k-1 neighbors that make up the anonymous area.At the same time,L_maxax and L_min are used to control the scope of the anonymous area.Then,accord to the pruning algorithm to remove the redundant grid,send randomly a candidate grid replacing the user's original position to satisfy k-anonymity.The experimental results show that the algorithm can improve the success rate of anonymity and maintain the quality of service reducing the query time.2)A location privacy-preserving algorithm based on string matching is proposed to solve the problem of the large area of k-anonymity area generated in space filling curve schemes.The Geohash coding of the user location in the Trie tree of the anonymous server is used to determine the k-1 neighbors that make up the anonymous area.Then,the anonymous area is selected with the hamming distance weights and the grid distance weights as the criterion to choose the smallest anonymous area achieving the k-anonymity effect.The experimental results show that the algorithm can effectively reduce the anonymous area,and further reduce the time overhead of the third-party anonymous server.