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.

Concerning the low accuracy and convergence of standard Particle Swarm Optimization (PSO) algorithm, an improved particle swarm optimization based on particle filter re-sampling and mutation named RSPSO was proposed. By using the resampling characteristic of abandoning particles with low weights and duplicating and retaining particles with high weights, an existing method for mutation was adopted to overcome the disadvantage of particle degeneracy, which greatly enhanced the local search capability in the later searching stage of PSO algorithm. RSPSO algorithm was compared with the standard algorithm and some other improved algorithms in the literature under different benchmark functions. The experimental results show that RSPSO has faster convergence, higher accuracy and better stability, and it is able to solve multi-modal problems globally.

The traditional graph-based recommendation algorithm neglects the combined time factor which results in the poor recommendation quality. In order to solve this problem, a personalized recommendation algorithm integrating roulette walk and combined time effect was proposed. Based on the user-item bipartite graph, the algorithm introduced attenuation function to quantize combined time factor as association probability of the nodes; Then roulette selection model was utilized to select the next target node according to those associated probability of the nodes skillfully; Finally, the top-N recommendation for each user was provided. The experimental results show that the improved algorithm is better in terms of precision, recall and coverage index, compared with the conventional PersonalRank random-walk algorithm.