A Novel Algorithm for Completely Hiding Sensitive Association Rules

With rapid advance of the network and data mining techniques, the protection of the confidentiality of sensitive information in a database becomes a critical issue when releasing data to outside parties' Association analysis is a powerful and popular tool for discovering relationships hidden in large data sets. The relationships can be represented in a form of frequent itemsets or association rules. One rule is categorized as sensitive if its disclosure risk is above some given threshold. Privacy-preserving data mining is an important issue which can be applied to various domains, such as Web commerce, crime reconnoitering, health care, and customer's consumption analysis. The main approach to hide sensitive association rules is to reduce the support or the confidence of the rules. This is done by modifying transactions or items in the database. However, the modifications will generate side effects, i.e., nonsensitive rule falsely hidden (i.e., lost rules) and spurious rules falsely generated (i.e., new rules). There is a trade-off between sensitive rules hidden and side effects generated. In this study, we propose an efficient algorithm, MAR, for fast hiding sensitive association rules (SAR). The algorithm can completely hide any given SAR by scanning database only once, which significantly reduces the execution time. Experimental results show that FHSAR outperforms previous works in terms of execution time required and side effects generated in most cases.