Privacy Protection in Social Networks Using l-Diversity

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• 作者：Liangwen Yu (18) (19) (20)
  Jiawei Zhu (18) (19) (20)
  Zhengang Wu (18) (19) (20)
  Tao Yang (18) (19) (20)
  Jianbin Hu (18) (19) (20)
  Zhong Chen (18) (19) (20)
  
• 关键词：socail network ; privacy preserving ; data publishing ; l ; diversity
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7618
• 期：1
• 页码：445-452
• 全文大小：443KB
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• 作者单位：Liangwen Yu (18) (19) (20)
  Jiawei Zhu (18) (19) (20)
  Zhengang Wu (18) (19) (20)
  Tao Yang (18) (19) (20)
  Jianbin Hu (18) (19) (20)
  Zhong Chen (18) (19) (20)
  
  18. Institute of Software, School of EECS, Peking University, China
  19. MoE Key Lab of High Confidence Software Technologies (PKU), China
  20. MoE Key Lab of Network and Software Security Assurance (PKU), China
  

文摘

With the increasing popularity of online social networks, such as twitter and weibo, privacy preserving publishing of social network data has raised serious concerns. In this paper, we focus on the problem of preserving the sensitive attribute of the node in social network data. We call a graph l-diversity anonymous if all the same degree nodes in the graph form a group in which the frequency of the most frequent sensitive value is at most $\frac{1}{l}$ . To achieve this objective, we devise an efficient heuristic algorithm via graphic l-diverse partition and also use three anonymous strategies(AdjustGroup, RedirectEdges, AssignResidue)to optimize the heuristic algorithm. Finally, we verify the effectiveness of the algorithm through experiments.