Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (10): 1665-1673.doi: 10.23940/ijpe.20.10.p18.16651673

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K-Community Anonymity Approach for Social Network Data

Guoqiang Gong, Xin Cao, Ye Jin, Xiaobo Ding, and Ke Lv*   

  1. School of Computer and Information, Three Gorges University, Yichang, 443002, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * E-mail address: gonggq_shh@163.com
  • About author:Guoqiang Gong is an associate professor of the school of computer & information of China Three Gorges University. He received M.S. degrees in control theory and control engineering from China Three Gorges University in 2005 and the Ph.D. degree from China Tongji University in 2010. His research interests include privacy preservation, advanced signal processing.
    Xin Cao is a master degree candidate of the School of computer & information of China Three Gorges University. His research interests include deep learning and social network data analysis.
    Ye Jin is a master of the School of computer & information of China Three Gorges University. His research interests include social network and privacy protection.
    Xiaobo Ding is an associate professor of the school of computer & information of China Three Gorges University. His research interests include computer application and big data processing.
    Ke Lv is a professor of the school of computer & information of China Three Gorges University. His research interests include information processing and big data analysis.

Abstract: Different from the traditional privacy protection of relational data, in this paper we focus on the protection of graph data in social networks. The problem of identity disclosure on graph data publication in social networks has caused an increase in attention and many existing methods of protecting graph data are based on the properties of the vertices. These methods can resist invasions such as degree attacks or neighbor attacks. Once an adversary knows the structural information of the graph, the probability of a vertex being recognized will be greatly increased. Considering the community structure of the graph, we propose a k-community anonymity model, in which the probability of an adversary identifying a vertex is no more than 1/k. We conduct our experiments in real social network datasets and compare it with the traditional k-degree anonymity model. The results show that the new protection scheme has better anonymous performance on resisting the structural attacks and a greater impact on the community structure in the graph.

Key words: privacy protection, social networks, graph structure, community anonymity