文章基本信息

标题：Conserving Seclusion In Distributed Data Mining Over Horizontally Partitioned Data
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作者：Kalli Srinivasa Nageswara Prasad ; S.V.Suryanarayana
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2013
卷号：4
期号：10-4
出版社：Seventh Sense Research Group
摘要：Data mining practices are utilized to find out hidden information from large databases. Among several data mining techniques, association rule mining is receiving more focus on the experts to discover correlations between items or itemsets efficiently. In distributed database atmosphere, the way the information is distributed plays a crucial part in the issue definition. The information could be distributed horizontally or vertically or in hybrid mode among different websites. There is an increasing demand for computing global association rules for the sources goes to different internet sites in ways that individual data isn't unmasked and mining source manager knows the global studies and their individual data only. In this paper a model is suggested which assumes a sign based safe total Gaussian technique to find distributed association rules with trusted party by protecting the privacy of the individual’s data if the data is distributed horizontally among different mining bases
关键词：Data Mining Distributed Database; Privacy Preserving Association Rule Mining; and Perturbation Technique