Research On Location Privacy Preservation Methods In The Internet Of Vehicles

Developing with the internet of vehicles,the services provided are increasingly diversified,which bring great convenience to people’s life.However,when a vehicle acquires location-based services(LBS),it is required to provide its real location,which implies the user’s private information.If the untrustworthy LBS servers utilize the location information,the privacy of vehicle users will be threated.Therefore,this thesis studies the problem of location privacy preservation in the internet of vehicles,and focuses on privacy preservation methods of vehicle users’ location and trajectory.In terms of the contradiction between the service quality and privacy preservation,the thesis studies the uniform performance measurement of location privacy preservation methods.From the perspective of the user,the distance between the reported location and the actual location is defined as the location information loss.From the perspective of the attacker,the distance between the location obtained by the attacker and the user’s actual location is defined as the location prediction deviation,which represents the degree of privacy preservation.According to the two defined measures,we analyze five kinds of location privacy preservation methods,and give the specific definition of location information loss and prediction deviation.By analyzing the simulation results and actual data,the effectiveness of the proposed measurement method is verified.The thesis studies the problem of location privacy preservation based on dummy locations,and proposes a vehicle location privacy-preservation method based on dummy locations under road restriction.In order to improve the validity of dummy locations under road restriction,entropy is used to represent the degree of anonymity,and the effective distance is introduced to represent the characteristics of location distribution.Based on that,we improve the dummy location selection algorithm by maximizing anonymous entropy and effective distance,which ensures the uncertainty and dispersion of dummy locations.The performance analysis and simulation results show that,the proposed location privacy preservation method can improve the validity of dummy locations,enhance the preservation of location privacy and protect location privacy of vehicles effectively.In terms of the long-term statistical attack(LSA)and location correlation attack(LCA)in the internet of vehicles,the thesis proposes a vehicle trajectory privacy-preservation method based on cache mechanism and dummy locations.In the proposed method,the cache is deployed in roadside unit(RSU)to reduce the information interaction between vehicle users and the LBS server.Thus,the LSA and LCA are prevented.Moreover,combined with the service semantics,we design the cache update mechanism based on dummy locations to protect location privacy and improve the cache hit rate.The performance analysis and simulation results show that the proposed method can resist the LSA and LCA while ensuring cache hit ratio,and protect trajectory privacy of vehicles effectively.Two vehicle location privacy-preservation methods proposed in this thesis can protect the location and trajectory privacy of vehicle users as well as guaranteeing the LBS quality,and have a good application prospect in LBS for the internet of vehicles.