文章基本信息

标题：Privacy-Preserving Data Mining (PPDM) Method for Horizontally Partitioned Data
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作者：Mohamed Awad Ouda ; Sameh A. Salem ; Ihab A. Ali 等
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2012
卷号：9
期号：5
出版社：IJCSI Press
摘要：Due to the increase in sharing sensitive data through networks among businesses, governments and other parties, privacy preserving has become an important issue in data mining and knowledge discovery. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. This paper proposes a solution for privately computing data mining classification algorithm for horizontally partitioned data without disclosing any information about the sources or the data. The proposed method (PPDM) combines the advantages of RSA public key cryptosystem and homomorphic encryption scheme. Experimental results show that the PPDM method is robust in terms of privacy, accuracy, and efficiency.
关键词：privacy preserving;data mining; K nearest neighbor; secure multi party computation