Research On Dummy Location For LBS Location Privacy Preservation

LBS users have to report their location information and query information to LBS server when they use the LBS service.If a malicious attacker gets those users' data,it can cause users' privacy leak.Therefore,how to preserve users' privacy while let users enjoy LBS service the same time is the current research hot spots.Dummy location is a classic method of privacy preservation for LBS location.The method realizes the privacy preservation of user' location privacy by releasing the dummy location to hide the real location.Dummy location will affect the user's service accuracy.Attacker can also exclude dummy location by prior knowledge.Therefore,how to generate a reasonable dummy location is an important issue in the field of LBS location privacy preservation.This thesis investigates the LBS location privacy preservation from the views of dummy location generation and the privacy threats under different application scenarios.Our main contributions are as follows:(1)This thesis proposes a dummy location generation method(DLSG)based on location semantics and geographic distance,which can effectively resist location privacy infer attack based on background information.The dummy location generation algorithms proposed by current researchers considers the conditions of pervasive,uniform,crowded and so on.The purpose is to generates the dummy location which appears reasonable in the view of attacker.However,under successive LBS requests,attackers can exclude these fake locations by considering user's movement patterns.Semantic information is an important background information of geographical location.Location semantics can effectively construct the user's mobile mode.The algorithm proposed in this thesis calculates the semantic distance between locations by the location mapping and calculating Mallows distance of the location-mapped trajectory.The location semantic class is constructed by combining the location geographic distance with the location semantic.Finally,the dummy location is selected from the location semantic classes.The experimental simulation shows that our algorithm can effectively resist the location inference attacks based on the user's movement patterns.(2)This thesis proposes a dummy query construction method for In LBS interest of points service,which can effectively resist location privacy infer attack based on user history inquiry.The method uses the DLSG algorithm to generate a set of dummy locations and randomly selecting a dummy location as the location of the query.By changing the scope of the query,this method minimizes the communication overhead while guaranteeing the service accuracy.The DLSG algorithm generates the dummy location,which can effectively resist the attack based on the user's movement pattern.By limiting the scope of the query,this paper proves that the algorithm satisfies the constraints of geographic difference privacy.Simulation results show that this algorithm can effectively protect the users' location privacy without affecting the service quality of users.(3)This thesis proposes a distributed dummy generation method based on the public goods game,to handle the problem of free-riding in the dummy generation.Dummies are beneficial to all users in the same area,and users tend to wait for other users to generate dummies,which results "free-riding" problem.In order to solve this problem,this thesis constructs a distributed dummy generating mechanism,which generates dummies through users' cooperation to meet the require of k-anonymity.An incentive mechanism is proposed to promote.the user cooperation and reduce "free riding" behavior.This paper also analyzes this model from the perspective of public goods game.It shows that the incentive mechanism proposed in this paper can effectively promote the cooperation of the users by comparing the cooperation rate with or without the incentive mechanism.Finally,the dynamic evolution simulation experiment verifies the game theory analysis.