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  • 标题:Fuzzy based approach for privacy preserving publication of data
  • 本地全文:下载
  • 作者:V. Valli Kumari ; S.Srinivasa Rao ; Kvsvn Raju
  • 期刊名称:International Journal of Computer Science and Network Security
  • 印刷版ISSN:1738-7906
  • 出版年度:2008
  • 卷号:8
  • 期号:1
  • 页码:115-121
  • 出版社:International Journal of Computer Science and Network Security
  • 摘要:Data privacy is the most acclaimed problem when publishing individual data. It ensures individual data publishing without disclosing sensitive data. The much popular approach, is K-Anonymity, where data is transformed to equivalence classes, each class having a set of K- records that are indistinguishable from each other. But several authors have pointed out numerous problems with K-anonymity and have proposed techniques to counter them or avoid them. l-diversity and t-closeness are such techniques to name a few. Our study has shown that all these techniques increase computational effort to practically infeasible levels, though they increase privacy. A few techniques account for too much of information loss, while achieving privacy. In this paper, we propose a novel, holistic approach for achieving maximum privacy with no information loss and minimum overheads (as only the necessary tuples are transformed). We address the data privacy problem using fuzzy set approach, a total paradigm shift and a new perspective of looking at privacy problem in data publishing. Our practically feasible method in addition, allows personalized privacy preservation, and is useful for both numerical and categorical attributes.
  • 关键词:Privacy preserving, data privacy, fuzzy information, anonymity
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