基于大数据的分布式隐私保护聚类挖掘算法研究

Research on distributed privacy protection clustering mining algorithm based on big data

随着云计算和移动互联网技术的迅猛发展,网络上每时每刻都产生海量的数据,在大数据时代,数据大多采用分布式存储,存储在多个相互独立的站点上,典型的数据挖掘算法已不适应大数据背景下数据挖掘的相关隐私数据安全方面的需求。在大数据背景下,进行分布式数据挖掘,面临着要解决数据安全保护和隐私泄露等方面的技术问题。因此,本文将同态加密技术应用于典型的K-means聚类挖掘算法,设计一种基于大数据的分布式隐私保护的PP-kmeans算法,实验表明,该算法可以实现数据隐私安全保护,并且达到较为精确的大数据聚类挖掘效果。

With the rapid development of cloud computing and mobile Internet technology, huge amounts of data are generated onthe network at every moment. In the era of big data, most of the data are designed in distributed storage, stored on manyindependent sites. The typical data mining algorithm can not meet the requirement of privacy data security in the big databackground. In the big data background, distributed data mining is faced with the need to solve data security, privacy disclosure andother technical problems. Therefore, this paper applies homomorphic encryption technology to typical K-means clustering miningalgorithm, and designs a distributed privacy protection PP - kmeans algorithm based on big data. Experiments show that thisalgorithm can realize data privacy security protection. And more accurate big data clustering mining effect is achieved.