A Novel Method for Protecting Sensitive Knowledge in Association Rules Mining

• Main Authors: En-Tzu Wang, 王恩慈
• Other Authors: Guanling Lee
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/70145166546301796919

碩士 === 國立東華大學 === 資訊工程學系 === 93 === In the researches of data mining, discovering frequent patterns from huge amounts of data is one of the most studied problems. The frequent patterns mined form databases can bring the users many commercial benefits. However, some sensitive patterns with security concerned may cause a threat to privacy. We investigate to find an appropriate balance between a need for privacy and information discovery on frequent patterns. In this thesis, a novel method for modifying databases to hide sensitive patterns is proposed. By multiplying the original database and a sanitization matrix together, a sanitized database with privacy concerns is obtained. Additionally, two probability policies are introduced to against the recovery of sensitive patterns and reduce the probability of hiding non-sensitive patterns in the sanitized database. The complexity analysis of our sanitization process is proved and a set of experiments is also performed to show the benefit of our approach.