计算机科学 ›› 2012, Vol. 39 ›› Issue (3): 163-169.

• 数据库与数据挖掘 • 上一篇    下一篇

分布式聚类算法的隐私保护研究

刘英华,杨炳儒,曹丹阳,马楠   

  1. (北京科技大学信息工程学院 北京100083);(中国青年政治学院 北京100089)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Research on Privacy Preserving Distributed Clustering Algorithm

LIU Ying-hua,YANG Bing-ru,CAO Dan-yang,MA Nan   

  • Online:2018-11-16 Published:2018-11-16

摘要: 隐私保护数据挖掘是在不精确访问原始数据的基础上,挖掘出准确的规则和知识。针对分布式环境下聚类挖掘算法的隐私保护问题,提出了一种基于完全同态加密的分布式聚类挖掘算法(FHE-DK-MEANS算法)。理论分析和实验结果表明,FHE-DK-MEANS算法不仅具有很好的数据隐私性,而且保持了聚类精度。

关键词: 数据挖掘,隐私保护,聚类,分布式数据

Abstract: Privacy preserving data mining is to discover accurate rules and knowledge without precise access to the raw data. I}his paper focused on privacy preserving clustering algorithms mining in a distributed environment, and presented a fully homomorphic encryption algorithm based on distributed k-means (FHE-DK-MEANS algorithm). Theoretical analysis and experimental results show that FHE-DK-MEANS algorithm can provide better privacy and accuracy.

Key words: Data mining,Privacy preserving,Clustering,Distributed data

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