In recent years, the use of data mining techniques and related applications has increased a lot as it is used to extract important knowledge from large amount of data. This has increased the disclosure risks to sensitive information when the data is released to outside parties. Database containing sensitive knowledge must be protected against unauthorized access. Seeing this it has become necessary to hide sensitive knowledge in database. To address this problem, Privacy Preservation Data Mining (PPDM) include association rule hiding method to protect privacy of sensitive data against association rule mining. In this paper, we survey existing approaches to association rule hiding, along with some open challenges. We have also summarized few of the recent evolution. Keywords Association rule hidingAn association rule is in the form X => Y, where X and Y are the subsets of item set in I, XI, YI, and X∩Y=Ø. In the rule X => Y, where X is called the antecedent (left-hand-side) and Y is the consequent (right-hand-side). Association rule mining generates high number of rules and only few of them are of interest. To solve interest measurement problem, minimum support and minimum confidence thresholds are
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