Research And Application Of Mobile Social Network Service Based On Location Privacy

Mobile social network is a deep integration of mobile,social and location.As mobile social network naturally combines location data with non-location data(user personal information,social relationship information,data mining information,etc.),these data are cross-reproduced.While users enjoy great convenience,they are also facing a huge threat of privacy leakage Location-related services include content sharing service,nearest neighbor query service and point of interest recommendation service.Protection of the user's location privacy is an inevitable choice to maintain a good ecosystem of mobile social networks.At present,domestic and foreign scholars have studied the privacy technologies related to mobile social networks.However,the existing research results still have the following shortcomings:1)Sharing location content involves the privacy of other users' locations,and publishers have no control over access to forwarded content.2)The aggregate nearest neighbor query easily exposes the location and trajectory privacy of the user group in continuous query,and the query response delay is high.3)The point of interest recommendation easily reveals the trajectory privacy of the user if the trajectory data source is directly mined and analyzed and the recommendation accuracy is low.In view of the above deficiencies,this paper studies the privacy and security related technologies in mobile social network services,and the work and innovations are as follows:Firstly,in order to protect the multi-party privacy interest involved in the content,a trust-based cooperative access control model is proposed.This model shields the differences in privacy policies between users,and considers the existence of conflicts in the merger decision-making.It adopts a conflict-based conflict resolution scheme that is more in line with user psychology,in which the concessions possibility is built around the trust between users,which is more in line with the characteristics of social networks;Aiming at the problem that the model can't resist the sensitive location of the service provider,a k-anonymity algorithm based on anti-location semantic attack is proposed,which makes the false location set guarantee the maximum entropy and constitutes the largest anonymous region.The experiment proves the availability of the algorithm.Secondly,a scheme of continuous aggregate nearest neighbor query based on trusted anonymous server is proposed.Specifically,a policy-optimized aggregate nearest neighbor query algorithm is adopted on the LBS server.The response speed of the results is improved by narrowing the searching range of POI and speeding up the pruning of non-nearest neighbors.A circle-based out-of-bounds detection algorithm is adopted on the anonymous server.The algorithm determines whether the query needs to be updated through the secure area,and avoids the user directly submitting the continuous movement track to the LBS server,thereby protecting the user's location and track privacy.Theoretical analysis and experimental data show that the proposed algorithms are more secure and perform better than existing peer algorithms.Finally,for the trajectory privacy leakage problem involved in the points of interest recommendation,this paper proposes a differential privacy trajectory analysis algorithm for recommending points of interest to users.The algorithm first converts the original trajectory data set into a user-location bipartite graph,and then converts the bipartite graph to the corresponding correlation matrix and adds noise obeying the Laplace distribution to the matrix to satisfy ?-differential privacy.Next,an execution of the HITS algorithm is performed on the data structure to generate an ordered list of recommended points of interest.Finally,adjust the parameters to find the balance between the accuracy of the recommendation results and the strength of the privacy protection.