Large-Scale Social Network Privacy Protection Method for Protecting K-Core

Abstract

Social network analysis has many important applications and methods which depend on the sharing and publishing of graphs. For example, link privacy requires limiting the probability of an adversary identifying a target sensitive link between two individuals in the published social network graph. However, the existing link privacy protection methods have low processing power for large-scale graph data and less consideration of community protection in the publishing graphs. Therefore, aiming at sensitive link privacy protection, a large-scale social network privacy protection model to protect K-Core (PPMPK) was proposed. The large-scale social network graph was processed to ensure that the core number and the community structure of the nodes were unchanged based on the Pregel parallel graph processing model. Extensive experiments on the real data sets showed that the proposed method could effectively process the large-scale graph data and protect the data availability of the published graphs, especially in community protection.