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Privacy-preserving data mining [Agrawal, R., et al., May 2000] has recently emerged to address one of the negative sides of data mining technology: the threat to individual privacy. For example, through data mining, one is able to infer sensitive information, including personal information or even patterns, from non-sensitive information or unclassified data. There have been two broad approaches for privacy-preserving data mining. The first approach is to alter the data before delivery to the data miner so that real values are obscured. The second approach assumes the data is distributed between two or more sites, and these sites cooperate to learn the global data mining results without revealing the data at their individual sites. Given specific rules to be hidden, many data altering techniques for hiding association, classification and clustering rules have been proposed. However, to specify hidden rules, entire data mining process needs to be executed. For some applications, we are only interested in hiding certain sensitive items. In this work, we assume that only sensitive items are given and propose two algorithms to modify data in database so that sensitive items cannot be inferred through association rules mining algorithms. Examples illustrating the proposed algorithms are given. The efficiency of the proposed approach is further compared with Dasseni etc. (2001) approach. It is shown that our approach required less number of databases scanning and prune more number of hidden rules. However, our approach must hide all rules containing the hidden items on the right hand side, where Dasseni's approach can hide specific rules.