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  • 标题:Research on Privacy Preserving on K-anonymity
  • 本地全文:下载
  • 作者:Pan, Yun ; Zhu, Xiao-ling ; Chen, Ting-gui
  • 期刊名称:Journal of Software
  • 印刷版ISSN:1796-217X
  • 出版年度:2012
  • 卷号:7
  • 期号:7
  • 页码:1649-1656
  • DOI:10.4304/jsw.7.7.1649-1656
  • 语种:English
  • 出版社:Academy Publisher
  • 摘要:The disclosure of sensitive information has become prominent nowadays; privacy preservation has become a research hotspot in the field of data security. Among all the algorithms of privacy preservation in data mining, K-anonymity is a kind of common and valid algorithm in privacy preservation, which can effectively prevent the loss of sensitive information under linking attacks, and it is widely used in various fields recent years. This article based on the existing K-anonymity privacy preservation of the basic ideas and concepts, K-anonymity model, and enhanced the K-anonymity model, and gives a simple example to compare each algorithm; finally, it prospected the development direction of K-anonymity on privacy preservation.
  • 关键词:data mining;privacy preservation;K-anonymity;generalization & suppression;the enhanced K-anonymity
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