Hiding sensitive patterns in association rules mining

Data mining techniques have been developed in many applications. However it also causes a threat to privacy. We investigate to find an appropriate balance between a need for privacy and information discovery on association patterns. We propose an innovative technique for hiding sensitive patterns. In our approach, a sanitization matrix is defined. By multiplying the original transaction database and the sanitization matrix, a new database, which is sanitized for privacy concern, is described. Moreover a set of experiments is performed to show the effectiveness of our approach.