A Novel Method for Protecting Sensitive Knowledge in Association Rules Mining

Abstract

Discovering frequent patterns from huge amounts of data is one of the most studied problems in data mining. However, some sensitive patterns with security policies may cause a threat to privacy. We investigate to find an appropriate balance between a need for privacy and information discovery on frequent patterns. By multiplying the original database and a sanitization matrix together, a sanitized database with privacy concerns is obtained. Additionally, a probability policy is proposed to against the recovery of sensitive patterns and reduces the modifications of the sanitized database. A set of experiments is also performed to show the benefit of our work.