New framework in Sensitive Rule Hiding
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - Issue 2012 by IJCTT Journal | ||
| Volume-3 Issue-1 | ||
| Year of Publication : 2012 | ||
| Authors :A.S. Naveenkumar ,Dr. M. Punithavalli. |
A.S. Naveenkumar ,Dr. M. Punithavalli."New framework in Sensitive Rule Hiding"International Journal of Computer Trends and Technology (IJCTT),V3(1):25-29 Issue 2012 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform this data into information. Privacy preserving data mining is a novel research direction in data mining and statistical databases, which has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. There have been two types of privacy proposed concerning data mining. The first type of privacy, called output privacy, is that the data is altered so that the mining result will preserve certain privacy. The second type of privacy, called input privacy, is that the data is manipulated so that the mining result is not affected or minimally affected. For output privacy in hiding association rules, current approaches require hidden rules or patterns to be given in advance. However, to specify hidden rules, entire data mining process needs to be executed. For some applications, only certain sensitive rules that contain sensitive items are required to hide. In this work, an algorithm ISSRH (Increase Support Sensitive Rule Hiding) is proposed, to hide the sensitive rules that contain sensitive items, so that sensitive rules containing specified sensitive items on the right hand side of the rule cannot be inferred through association rule mining.
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Keywords— Data Mining, Privacy Preserving, Association Rules, Sensitive Rules, Clustering, Minimum Support, Minimum confidence