Protecting Social Network With Differential Privacy Under Novel Graph Model

Abstract

Online social networks (OSNs) contain sensitive information about individuals, so it's important to anonymize network data before releasing it. Recently, researchers introduced differential privacy to give a strict privacy guarantee. Graph abstraction models are essential to transform graph structural information into numerical type data, and the choice of models may influence the utility preservation of the published graph. In this paper, we propose a comprehensive differentially private graph model which 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 series data makes the regeneration process more executable and effective, while high dimensional data preserves additional utility of the graph. As the higher dimensional data is more sensitive to the noise, we carefully design the executing sequence and add three levels of rewiring algorithms to further preserve the structural information. The final released graph increases the graph utility under differential privacy. We also experimentally evaluate our approach on real-world OSNs and show that our scheme produces ready-to-be-shared graphs that are closely matched with the originals, while achieving differential privacy.

Keywords

• Social network data publishing
• anonymization
• differential privacy
• dK graph abstraction model