Research And Implementation Of Privacy Protection Based On Point Of Interest Query In Road Network

With the popularity of 5G network and mobile Internet,location-based service(LBS)is more and more widely used in people's life,such as point of interest query,route query,location-based mobile social network and so on.However,while LBS services bring convenience to people,there is also a huge risk of privacy leakage.On the one hand,users may disclose sensitive information such as location and query content when using LBS services.On the other hand,LBS service providers may not be trusted,and it is possible to disclose users' sensitive information to a third party.In order to protect the user's location privacy,many location privacy protection methods have been produced,but most of them are only applicable to European space,and the effect of location privacy protection is poor in the road network environment.Therefore,how to ensure the user's location privacy security in the road network environment while using LBS service is an urgent problem to be solved at present.In this paper,the location privacy protection of interest point snapshot query and continuous query in road network is deeply studied,and the following research results are obtained:1.An enhanced privacy protection of snapshot query based on multi-user collaboration(EPP-MUC)in road network is proposed to solve the problem of low query efficiency caused by high complexity of encryption algorithm in the privacy protection process of snapshot query of points of interest.Firstly,the multi-user tree collaborative enhancement domain is established,and the user cache adopts B+index tree structure to improve the retrieval efficiency.Then,the content of interest points is searched in the collaborative user cache according to the multi-user access rules.If the cache fails to hit,the encrypted retrieval request is sent to the LBS server to obtain the required services.This method not only protects the privacy of collaborative users,but also improves the query efficiency by reducing the interaction between users and LBS server.2.A trajectory privacy protection of multiple anonymizers based on trajectory prediction(DTP-MA)is proposed to solve the problems of poor privacy protection and low query efficiency.In this method,the longest common subsequence algorithm is used to process the user trajectory data around the road to predict the next moving location of the user,the client saves the information of interest points of current location and predicted location to improve the query efficiency.The pseudonym technology is used to cut off the correlation between user trajectory,query content and user identity.Using anchor point technology instead of the real location of the user to initiate the query;Multiple anonymous selection mechanism is used to select different anonymous to prevent the user trajectory leakage caused by the location of the anchor point queried by the user at different times being located in the same anonymous.This method can better protect the user trajectory and has better query efficiency in the privacy protection of continuous query trajectory.3.The two proposed location privacy protection methods are simulated.The experimental results show that the EPP-MUC method improves the query efficiency while considering the location privacy of users and cooperative users;the DTP-MA method has higher query efficiency and stronger trajectory privacy protection effect in the continuous query trajectory privacy protection method.