Association rule hiding using integer linear programming

<span>Privacy preserving data mining has become the focus of attention government statistical agencies and database security research community who are concerned with preventing privacy disclosure during mining. Repositories large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one powerful techniques for knowledge exists in before it is published. In this paper, we present a constraint-based optimization approach set rules, using well-structured integer linear program formulation. The proposed reduces sanitization problem an instance programming problem. solution determines transactions sanitized order conceal while minimizing impact on non-sensitive rules. We also heuristic algorithm performs by reducing support or confidence results experimental evaluation real-life indicate promising performance terms side effects original database.</span>