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Semi-Edge Anonymity: Graph Publication when the Protection Algorithm Is Available

  • Mingxuan Yuan
  • Lei Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7238)

Abstract

With the popularity of social networks, the privacy issues related with social network data become more and more important. The connection information between users, as well as their sensitive attributes, should be protected. There are some proposals studying how to publish a privacy preserving graph. However, when the algorithm which generates the published graph is known by the attacker, the current protection models may still leak some connection information. In this paper, we propose a new protection model, “Semi-Edge Anonymity”, to protect both user’s sensitive attributes and connection information even when an attacker knows the publication algorithm. Moreover, any state-of-art tabular data protection techniques can be applied to Semi-Edge Anonymity model to protect sensitive attributes. We theoretically prove that on two utilities, the possible world size and the true edge ratio, the Semi-Edge Anonymity model outperforms any clustering based model which protects links. We further conduct extensive experiments on real data sets for two other utilities. The results show that our model also has better performance on these utilities than the clustering based models.

Keywords

Simulated Annealing Algorithm Cluster Base Model Sensitive Attribute Super Node Social Network Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mingxuan Yuan
    • 1
  • Lei Chen
    • 1
  1. 1.Department of Computer Science and EngineeringThe Hong Kong University of Science & TechnologyHong kong

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