Abstract
As the growth of computer technology has been advanced, the amount of data has been increasing with an extremely fast rate. A variety of methods for knowledge discovery and data mining have been developed to help people digest the huge number of data. One of the popular data mining research issues is association rule mining. Based on the techniques for mining association rules, the correlations between data items can be identified. However, the misuses of these methods may bring undesired side effects to the people. Recently, researchers have made great efforts at hiding association rules. In this thesis, we develop a new approach that can hide the sensitive information without generating undesired side effects. Our approach consists of three steps corresponding to three possible problems. At first, we adopt the template concept to identify either the set of modifiable transactions or the set of probably affected association rules. For efficiency, we design indexing facilities for fast retrieval of the required information in the transaction database. Second, among the selected transactions for hiding sensitive rules, we further select the transactions that will not hide any of the non-sensitive rules. At the third step, we examine these selected transactions to avoid generating extra rules. Iteratively, sensitive rules can be hidden and the undesired side effects are avoided. In the experiments, we show the effectiveness of our approach according to the three conditions and analyze the performance of different methods for database modifications. Moreover, the results also show that our proposed approach has perfect scalability to the database size. Specifically, the time of the approach that considers all the three conditions is just a little bit slower than the time of the one that do not consider the two side effects.