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  • 标题:Privacy preserving Data mining methods
  • 本地全文:下载
  • 作者:S.M.Dolas ; V.M.Thakare ; Y.M.Kurwade
  • 期刊名称:International Journal of Electronics, Communication and Soft Computing Science and Engineering
  • 印刷版ISSN:2277-9477
  • 出版年度:2018
  • 卷号:11
  • 语种:English
  • 出版社:IJECSCSE
  • 摘要:Sensitive original information/data is extract of protected databases whether it is received or cut from the original database. The purpose of doing this is to prevent individual privacy against adverse data received. This paper focused on five different techniques such as tree framework, PPDM, Association mining rule, Rampart framework, anonymizing trajectories. . But some problems are persisting in each method. The paper proposes the method to overcome the existing problems and the improved method “Secure multi party computation and restricted data” is proposed in this paper.
  • 关键词:Data mining;Metrics;Association rules;Data collector;Suppression.
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