A new method for preserving privacy in quantitative association rules using DSR approach with automated generation of membership function
K. Sathiyapriya, G. Sudha Sadasivam, N. Celin · 2011
Data mining is the process of extracting hidden patterns from data. With the explosion of data at a tremendous rate, data mining is essential to extract useful information. Association rule mining is a method of finding correlation relationships 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 limited to binary items. But, real world data 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. A framework for automated generation of membership function is also proposed. Experimental results of the proposed approach demonstrate efficient information hiding with minimum side effects.