Security and Privacy in New Computing Environments. Second EAI International Conference, SPNCE 2019, Tianjin, China, April 13–14, 2019, Proceedings

Research Article

Research on Social Networks Publishing Method Under Differential Privacy

  • @INPROCEEDINGS{10.1007/978-3-030-21373-2_6,
        author={Han Wang and Shuyu Li},
        title={Research on Social Networks Publishing Method Under Differential Privacy},
        proceedings={Security and Privacy in New Computing Environments. Second EAI International Conference, SPNCE 2019, Tianjin, China, April 13--14, 2019, Proceedings},
        proceedings_a={SPNCE},
        year={2019},
        month={6},
        keywords={Differential privacy Social network Hierarchical random graph Data publishing Privacy protection},
        doi={10.1007/978-3-030-21373-2_6}
    }
    
  • Han Wang
    Shuyu Li
    Year: 2019
    Research on Social Networks Publishing Method Under Differential Privacy
    SPNCE
    Springer
    DOI: 10.1007/978-3-030-21373-2_6
Han Wang1,*, Shuyu Li1,*
  • 1: Shaanxi Normal University
*Contact email: wanghan@snnu.edu.cn, lishuyu@snnu.edu.cn

Abstract

Data publishing for large-scale social network has the risk of privacy leakage. Trying to solve this problem, a differential private social network data publishing algorithm named DP-HRG is proposed in the paper, which is based on Hierarchical Random Graph (HRG). Firstly, the social network is divided into 1-neighborhood subgraphs, and the HRG of each subgraph is extracted by using both Markov Monte Carlo (MCMC) and exponential mechanism to compose the HRG candidate set. Then an average edge matrix is obtained based on the HRG candidate set and perturbed by a random matrix. Finally, according to the perturbed average edge matrix, a 1-neighborhood graph is regenerated and pasted into the original social network for publishing. Experimental results show that the proposed algorithm preserves good network characteristics and better data utility while satisfying the requirement of privacy protection.