Concerning the problem of privacy leak of personal location when querying the nearest neighbor location in social network, a geo-indistinguishability mechanism was used to add random noise to the location data, and a privacy budget allocation method was proposed. First, the spatial regions were divided into grids, and the personalized privacy budget allocation was performed according to the location hits of user in different regions. Then, in order to solve the problem of low hit rate of the neighbor query in the disturbance location dataset, a Combined Incremental Neighbor Query (CINQ) algorithm was proposed to expand the search range of the demand space, and the combination query was used to filter out the redundancy data. Simulation results show that compared with the SpaceTwist algorithm, the CINQ algorithm had the query hit rate increased by 13.7 percentage points. Experimental results verify that the CINQ algorithm effectively solves the problem of low query hit rate caused by the location disturbance of the query target, and it is suitable for neighbor queries for disturbed locations in social network applications.

To solve the challenge of accurate user group discovering, a user topic discovering algorithm based on trust chain, which was composed by three steps, i.e., topic space discovering, group core user discovering and topic group discovering, was proposed. Firstly, the related definitions of the proposed algorithm were given formally. Secondly, the topic space was discovered through the topic-correlation calculation method and a user interest calculation method for topic space was addressed. Further, the trust chain model, which was composed by atomic, serial, and parallel trust chains, and its trust computation method of topic space were presented. Finally, the detail algorithms of topic group discovering, including topic space discovering algorithm, core user discovering algorithm and topic group discovering algorithm, were proposed. The experimental results show that the average accuracy of the proposed algorithm is 4.1% and 11.3% higher than that of the traditional interest-based and edge density-based group discovering methods. The presented algorithm can improve the accuracy of user group organizing effectively, and it will have good application value for user identifying and classifying in social network.