Use EMO to Protect Sensitive Knowledge in Association Rule Mining by Removing Items


When people utilize data mining techniques to discover useful knowledge behind large database, they also have the requirement to preserve some information so as not to be mined out, such as sensitive frequent item sets, rules, classification tree and the like. A feasible way to address this problem is to sanitize the database to conceal the sensitive information. In this paper, we focus on privacy preserving in association rule mining. In light of the tradeoff between hiding sensitive rules and disclosing non-sensitive ones during hiding process, we tackle this problem from a point view of multi-objective optimization. A novel association rule hiding approach was proposed based on evolutionary multi-objective optimization (EMO) algorithm. It adopted the model of hiding sensitive rules by deleting some items in database. Three side effects, including sensitive rules not hidden, non-sensitive lost rules and spurious rules were formulated as objectives to be minimized. The EMO algorithm was utilized to find a suitable subset of transactions to modify so that the three side effects can be minimized. Experiment results were reported to show the effectiveness of the proposed approach.