Social Network Data Publishing Model Satisfying Differential Privacy

Abstract

Social networks contain sensitive information, and direct press conferences disclose people’s privacy. The published social network data needs privacy protection. The currently released social network data has seriously damaged the network structure due to privacy protection. This paper proposes a social network graph publishing model that preserves the social network structure. This model builds an uncertainty graph based on privacy protection probability to generate social network data for publication. It is proved theoretically that the social network graph publishing model satisfies the definition of differential privacy. This paper uses three real social network datasets to evaluate the effectiveness of the proposed method, showing that the method effectively retains the network structure.