Novel Algorithms for Privacy Preserving Utility Mining

• Main Authors: Po-Chiang Hsu, 許柏強
• Other Authors: Jieh-Shan Yeh
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/26222401050471659004

碩士 === 靜宜大學 === 資訊管理學系研究所 === 95 === Privacy Preserving Data Mining (PPDM) is a popular research direction in data mining, but so far privacy preserving utility mining was not discussed. How to strike a balance between privacy protection and knowledge discovery in the sharing process is an important issue. This study focuses on the privacy preserving utility mining and present two effective algorithms, HHUIF and MSICF, to achieve the goal of hiding sensitive itemsets so that the adversaries can not mine them from the modified database. In addition, we minimize the impact on the sanitized database in the process of hiding sensitive itemsets. In our experimental results, HHUIF has the lower miss costs than MSICF does in two synthetic datasets. Besides, MSICF has the lower difference between the original and sanitized databases than HHUIF has, except in the case where MinUtility = 4000.