Reseach On Privacy Protection Of Location And Trajectory In LBS

The developments in positioning and mobile communication technology have made applications that use Location-Based Service(LBS) increasingly popular. Nowadays LBS plays an important role in our daily life. Using LBS, users can easily get the information they want by using the hand-held terminals(e.g., Smart phones, tablets). For example, users can find the nearest restaurant or hospital. Since the LBS provider(LP) may be malicious and not credible, preserving user privacy while using LBS is an important issue. Additionally people are becoming more and more concerned about their privacy.For privacy reasons and due to lack of trust in the LBS provider, k-anonymity and l-diversity techniques have been widely used to preserve user privacy in distributed LBS architectures. However, in reality, there exist scenarios where the user locations are identical or similar/near each other. In such a scenario the k locations selected by k-anonymity technique are the same and location privacy can be easily compromised or leaked. Morever, users may send service requests continuously for a period of time. Because of the correlation of various positions in the continuous queries, traditional k-anonymity technology is no longer suitable for continuous queries. There is no guarantee that the uses’ trajectories have not been exposed. Therefore, protecting user location and trajectory information in LBS is an important issue need to be solved.The main contribution and innovation of this thesis is shown as follows:(1)To address the problem where the locations of k users are nearby, similar or identical, we propose the concept of location-labels to distinguish mobile user locations to sensitive locations and ordinary locations, and design a location-label based(LLB) algorithm for protecting user privacy. We propose three protocols including the request aggregation protocol, the pseudo-ID exchange protocol and the improved PLAM protocol in the LLB algorithm, which help in reducing the response time of the LBS system.(2)This thesis studies three kinds of attack models that can expose a user’s trajectory or path while the user is sending continuous queries to a LBS server, constructs a novel LBS system model for privacy preserving, and proposes the k-anonymity trajectory(KAT) algorithm which is suitable for both single query and continuous queries. Different from existing works, the KAT algorithm selects k-1 dummy locations using the sliding window based k-anonymity mechanism when the user is making single query, and selects k-1 dummy trajectories using the trajectory select mechanism for continuous queries.(3)This thesis evaluates the effectiveness of the proposed algorithms(LLB and KAT) by conducting simulations for the different scenarios. The simulation results show that the proposed algorithm can better protect privacy of users and has less time complexity than existing approachs.