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  • 标题:Enhanced P-Sensitive K-Anonymity Models for Privacy Preserving Data Publishing
  • 本地全文:下载
  • 作者:Xiaoxun Sun ; Hua Wang ; Jiuyong Li
  • 期刊名称:Transactions on Data Privacy
  • 印刷版ISSN:1888-5063
  • 电子版ISSN:2013-1631
  • 出版年度:2008
  • 卷号:1
  • 期号:2
  • 页码:53-66
  • 出版社:IIIA-CSIC
  • 摘要:

    Publishing data for analysis from a micro data table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing significance today. The k-anonymity model was proposed for privacy preserving data publication. While focusing on identity disclosure, k-anonymity model fails to protect attribute disclosure to some extent. Many efforts are made to enhance the k-anonymity model recently. In this paper, we propose two new privacy protection models called (p, α)-sensitive k-anonymity and (p+, α)-sensitive k-anonymity, respectively. Different from previous the p-sensitive k-anonymity model, these new introduced models allow us to release a lot more information without compromising privacy. Moreover, we prove that the (p, α)-sensitive and (p+, α)-sensitive k-anonymity problems are NP-hard. We also include testing and heuristic generating algorithms to generate desired micro data table. Experimental results show that our introduced model could significantly reduce the privacy breach.

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