Dynamic social privacy protection based on graph mode partition in complex social network

Abstract

Differential privacy protection model provides strict and quantitative risk representation for privacy disclosure, which greatly ensures the availability of data. However, most existing methods do not consider the semantic context, so they are vulnerable to attacks based on semantic information. Therefore, dynamic social privacy protection based on graph pattern partitioning is designed to satisfy differential privacy protection. Firstly, the structure of social network is represented as a graph model, and the original graph is classified into several sub-graphs according to the characteristics of nodes. Then, the dense area of each sub-graph is divided by quad-tree method, and the noise of differential privacy protection is added to the leaf nodes of the tree, and the graph publishing is generated by sub-graph reconstruction. Finally, the feasibility and practicability of the model are verified by statistical analysis, such as degree distribution, shortest path, and clustering coefficient. The simulation results show the validity and applicability of the privacy protection method proposed in this paper.