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  • 标题:Privacy Preserving Two-Party Hierarchical Clustering Over Vertically Partitioned Dataset
  • 本地全文:下载
  • 作者:Animesh Tripathy ; Ipsa De
  • 期刊名称:Journal of Software Engineering and Applications
  • 印刷版ISSN:1945-3116
  • 电子版ISSN:1945-3124
  • 出版年度:2013
  • 卷号:06
  • 期号:05
  • 页码:26-31
  • DOI:10.4236/jsea.2013.65B006
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要:Data mining has been a popular research area for more than a decade. There are several problems associated with data mining. Among them clustering is one of the most interesting problems. However, this problem becomes more challenging when dataset is distributed between different parties and they do not want to share their data. So, in this paper we propose a privacy preserving two party hierarchical clustering algorithm vertically partitioned data set. Each site only learns the final cluster centers, but nothing about the individual’s data.
  • 关键词:Data Mining; Privacy; Hierarchical; Clustering
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