Social Network Sensitive Area Perturbance Method Based on Firefly Algorithm

Abstract

Social network analysis has many essential applications, but it depends on sharing and publishing the underlying graph. The real dataset published by social networks has great appeal to researchers and research institutions. However, publishing datasets can create many security and privacy problems. A standard technique for achieving link privacy is to randomize a link over the space for node pairs based on probabilistically. Aiming at the problem of social network link privacy, a Sensitive Area Perturbance Based on Firefly (SAPBF) algorithm is proposed. Improve the data availability of the published graph by limiting the range of random perturbations. The specific method is to measure the influence of nodes, such as k-core, degree, and PageRank algorithms to obtain nodes with different influences. The firefly algorithm is applied to the social network to find high-influence nodes. The low-influence nodes are gathered around the high-influence nodes to form sensitive areas. Finally, according to different edge retention probability, the random perturbation algorithm protects privacy in the sensitive area. The content of the social network is continuously enriched and the scale is increasing. The performance of the anonymous algorithm of the single-machine social network graph is limited. The SAPBF algorithm is parallelized based on the Pregel model. The results of the algorithm in the real experimental dataset show that the proposed algorithm guarantees the property of the graph while ensuring the anonymity. Compared with the traditional random perturbation algorithm, the distributed processing graph improves the efficiency while ensuring the data availability of the graph.