Privacy preserving association rule hiding using border based approach

<div>Association rule mining is a well-known data technique used for extracting hidden correlations between items in large databases. In the majority of situations, results contain sensitive information about individuals and publishing such will violate individual secrecy. The challenge association to preserve confidentiality rules when releasing database external parties. hiding conceals knowledge extracted by modifying database. this paper, we introduce border-based algorithm rules. main purpose approach conceal set while maintaining utility at highest level. performance terms side effects demonstrated using experiments conducted on two real datasets. show that loss minimized without sacrificing accuracy. </div>