文章基本信息

标题：A Generalized Association Rule Based Method for Privacy Preserving in Data Mining
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作者：Vijay Patidar ; Vikash Shrivastava ; Vivek Shrivastava 等
期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：9
出版社：S.S. Mishra
摘要：Mining association rules from huge amounts of data is an important issue in data mining, with the discovered information often being comm ercially valuable. Moreover, companies that conduct similar business are often willing to collaborate with each other by mining significant knowledge patterns from the collaborative datasets to gain the mutual benefit. However, in a cooperative project, some of these companies may want certain strategic or private data called sensitive patterns not to be published in the database. Therefore, before the database is released for sharing, some sensitive patterns have to be hidden in the database because of pri vacy or security concerns. To solve this problem, sensitive-knowledge-hiding (association rules hiding) problem has been discussed in the research community working on security and knowledge discovery. The aim of these algorithms is to extract as much as non sensitive knowledge from the collaborative databases as possible while protecting sensitive information. Sensitive-knowledge-hiding problem was proven to be a nondeterministic polynomial-time hard problem. After that, a lot of research has been completed to solve the problem. In this article, we will introduce a new modified hybrid algorithm for privacy preserving. There are many approaches to hide certain association rules which take the support and confidence as a base for algorithms ([1, 2, 6,7]). Our approach is a modification of work done by [7]. Our algorithm takes lesser number of passes to hide a specific association rule
关键词：Association Rule Mining; Sensitive Rule Hiding; Support; Confidence