|
Twitter
|
Facebook
|
Google+
|
VKontakte
|
LinkedIn
|
 
 
International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Tuesday 23 October 2018

About IJISR

News

Submission

Downloads

Archives

Custom Search

Contact

Connect with IJISR

   
 
 
 

Differential Evolution Algorithm for Hiding Fuzzy Association Rules Using Mutual Information


Volume 24, Issue 2, June 2016, Pages 388–396

 Differential Evolution Algorithm for Hiding Fuzzy Association Rules Using Mutual Information

K. Sathiyapriya and G. Sudha Sadasivam

Original language: English

Received 14 February 2016

Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


Data mining is the process of extracting the useful information from the large amount of available data. Association rule mining is a popular tool for discovering useful associations from large amount of data. Once private data is released for mining, it is very difficult to prevent its misuse. Useful associations with hidden information or knowledge that are sensitive to the database owner could be easily exposed using this kind of tool. Therefore it is necessary to hide all the sensitive information that can be mined from the data in the form of association rules before releasing the data. Most of the methods proposed in literature for association rule hiding deals with binary database and few methods for quantitative database suffer with side effects. This paper proposes an approach for hiding sensitive association rules using differential evolution and mutual information. The proposed algorithm hides the rule by decreasing Association Measure of the rule below threshold. Side effects are reduced by choosing the items with higher mutual information. Experimental results on real datasets demonstrate that the proposed method can effectively sanitize the sensitive data with fewer side effects to the non-sensitive data.

Author Keywords: Sensitive rules, Mutual Information, lost rules, Ghost ules, Fuzzy, Rule hiding, Differential evolution.


How to Cite this Article


K. Sathiyapriya and G. Sudha Sadasivam, “Differential Evolution Algorithm for Hiding Fuzzy Association Rules Using Mutual Information,” International Journal of Innovation and Scientific Research, vol. 24, no. 2, pp. 388–396, June 2016.