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A New Method for preserving privacy in Quantitative Association Rules using Genetic Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 60 - Number 12 |
| Year of Publication: 2012 |
| Authors: K. Sathiya Priya, G. Sudha Sadasivam, V. B. Karthikeyan |
10.5120/9743-4295
|
K. Sathiya Priya, G. Sudha Sadasivam, V. B. Karthikeyan . A New Method for preserving privacy in Quantitative Association Rules using Genetic Algorithm. International Journal of Computer Applications. 60, 12 ( December 2012), 12-19. DOI=10.5120/9743-4295
Abstract
Data mining is the process of extracting hidden patterns from data. With the explosion of data, data mining is essential to extract useful information. Association rule mining is a method for finding correlation among large set of data items. A rule is characterized as sensitive if its disclosure risk is above a certain confidence value. Sensitive rules should not be disclosed to the public, as they can be used to infer sensitive data and provide an advantage for the business competitors. Techniques for hiding association rules are almost limited to binary items. But, real world data mostly consists of quantitative values. In this paper, a method to hide fuzzy association rule is proposed, in which, the fuzzified data is mined using modified apriori algorithm in order to extract rules and identify sensitive rules. The sensitive rules are hidden by decreasing the support value of Right Hand Side(RHS) of the rule. Genetic algorithm is used to ensure security of the database and keep the utility and certainty of the mined rules at highest level. Experimental results of the proposed approach demonstrate efficient information hiding with less side effects.
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