Sharing Social Networks Using a Novel Differentially Private Graph Model

Abstract

Online social networks (OSNs) often contain sensitive information about individuals. Therefore, anonymizing social network data before releasing it becomes an important issue. Recent research introduces several graph abstraction models to extract graph features and add sufficient noise to achieve differential privacy.In this paper, we design and analyze a comprehensive differentially private graph model that combines the dK-1, dK-2, and dK-3 series together. The dK-1 series stores the degree frequency, the dK-2 series adds the joint degree frequency, and the dK-3 series contains the linking information between edges. In our scheme, low dimensional data makes the regeneration process more executable and effective, while high dimensional data preserves additional utility of the graph. As the higher dimensional model is more sensitive to the noise, we carefully design the executing sequence. The final released graph increases the graph utility under differential privacy.