期刊文献+

分布式聚类算法的隐私保护研究 被引量:2

Research on Privacy Preserving Distributed Clustering Algorithm
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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。 Privacy preserving data mining is to discover accurate rules and knowledge without precise access to the raw data. This 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.
出处 《计算机科学》 CSCD 北大核心 2012年第3期160-162,共3页 Computer Science
基金 国家自然科学基金项目(60875029) 北京市科技计划专项课题资助
关键词 数据挖掘 隐私保护 聚类 分布式数据 Data mining, Privacy preserving, Clustering, Distributed data
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参考文献10

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二级参考文献13

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