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Customization of Sensitive Association Rules in Data Mining

Manish Vyas, Vikas Choudhary

Abstract


OLAP databases contain confidential rules that must be protected before published, association rule hiding becomes one of important
privacy preserving data mining problems. Compared with traditional data modification methods (data distortion and data blocking),
data reconstruction is a new promising, but not sufficiently investigated method, which is inspired by the inverse frequent set mining
problem. A number of techniques have been cited in the literature provided by various researchers which focus on hiding the
sensitive association rules to prevent them to reach the unauthorized segment, but no significant contributions are seen to provide
the customized association rules to the unintended segment. My research focuses on proposing a new knowledge sanitization
algorithm as well as FP-tree based method for inverse frequent set mining, which can be used in our proposed reconstruction-based
framework

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