Location Privacy Protection Based On Path Planning

With the development of mobile networks and positioning technologies, location based service is becoming more and more popular, such as location-aware emergency response, advertisement, and car navigation system etc. However, while people enjoy more convenient life provided by location based services, their location privacy may leak. To tackle this problem, many researchers propose different ways to make users' privacy preserving and most of them focus on how to confuse users'true identities, or hide users'exact position while a user is in a cloaking square. The representative method is K-anonymity based method which requires not less than k users in the same square when there is a location based service request. In case there are not enough users in this cloaking square, the condition of the k-anonymity is not satisfied so location privacy would be leak.In this paper, we would tackle the problem of location privacy in a different way by predicating a safe way for users. Our objective is to provide more guarantee of privacy preserving for users under the K-anonymity criteria. We propose a path predicting algorithm from a user's current position to his/her destination. Every point on such path satisfies a location privacy policy under K-anonymity criteria. We also consider lower limit and upper limit provided by users which enhances the extent of k-anonymity so as to balance privacy requirement and performance. Furthermore, the path would be dynamic adjusted according to users'movement and environment changes.When the query users arise to very large, the server will suffer computational load and reduce efficiency. To tackle above issues, we propose an improved distributed path prediction service models and algorithms. The architecture consists of trusted central server and distributed servers. First, the trusted central server selects a distributed server list which will predict path for users. Then the distributed server exchanged message through Handshake Protocol. The distributed servers in the list plan a k-anonymity safe path for users. The computing model reduces the central server's computational burden and improves service efficiency.Evaluation experiments are performed to achieve and verify our method. The experiment results show that our method is correct and effective.