Hiding fuzzy association rules in quantitative data

Abstract

Data mining and knowledge discovery from databases are researches in which unknown associations automatically discovered from big amounts of data. Advances in data collection, data distribution and related technologies caused researchers to investigate current data mining algorithms from a new point of view. This is personal privacy. With the increase in researches on data mining and sharing of knowledge with many people thru the internet and media, personal privacy problems are considered more seriously. Many techniques have been recently developed against bad purposed data mining. These techniques are classified into different categories. In the first of these categories, called input privacy, the data is manipulated, and the mining result is not affected or minimally affected. The second type of privacy is called as output privacy, where the data is altered. This change makes the mining result preserving certain privacy. In output privacy, specific rules that should be hidden are given in advance. According to this constraint, many data altering techniques for hiding association, classification and clustering rules have been proposed in the literature. However, almost all of them have been done on binary items. But, in real world, the data mostly consist of quantitative values. In this paper, we propose a novel method to hide critical fuzzy association rules from quantitative data. For this purpose, we increase support value of LHS of the rule to be hidden. Experimental results demonstrate the performance and output effects of the proposed algorithm. © 2008 IEEE.