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文章基本信息

  • 标题:A Knowledge Model Sharing Based Approach to Privacy-Preserving Data Mining
  • 本地全文:下载
  • 作者:Hongwei Tian ; Weining Zhang ; Shouhuai Xu
  • 期刊名称:Transactions on Data Privacy
  • 印刷版ISSN:1888-5063
  • 电子版ISSN:2013-1631
  • 出版年度:2012
  • 卷号:5
  • 期号:2
  • 页码:433-467
  • 出版社:IIIA-CSIC
  • 摘要:

    Privacy-preserving data mining (PPDM) is an important problem and is currently studied in three approaches: the cryptographic approach, the data publishing, and the model publishing. However, each of these approaches has some problems. The cryptographic approach does not protect privacy of learned knowledge models and may have performance and scalability issues. The data publishing, although is popular, may suffer from too much utility loss for certain types of data mining applications. The model publishing is lacking of efficient algorithms for practical use in a multiple data source environment.

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