Effective sanitization approaches to protect sensitive knowledge in high-utility itemset mining

摘要

For mutual benefit, data is shared among business organizations. However, this may result in privacy and security threats. To address this issue, privacy-preserving data mining is presented to sanitize the original database to hide all sensitive knowledge. Privacy-preserving utility mining is an extension of privacy-preserving data mining, the objective of which is to hide all sensitive high-utility itemsets and minimize the side effects on non-sensitive knowledge caused by the sanitization process. In this paper, three heuristic algorithms for privacy-preserving utility mining are proposed, namely, Selecting Maximum Utility item first (SMAU), Selecting Minimum Utility item first (SMIU) and Selecting Minimum Side Effects item first (SMSE). The quality of the database is well maintained because all of the proposed algorithms consider the side effects on the non-sensitive itemsets. Furthermore, to avoid performing multiple database scans, two table structures, T-table and HUI-table, are adopted to accelerate the hiding process by only scanning the database twice. The experimental results show that the proposed approaches successfully conceal all sensitive itemsets with fewer distortions of non-sensitive knowledge. Moreover, the influence of the database density on the proposed approaches is observed.