Privacy Preserving Association Rule Mining by Concept of Impact Factor using Item Lattice

Association Rules revealed by association rule mining may contain some sensitive rules, which may cause potential threats towards privacy and protection. Association rule hiding is a competent solution that helps enterprises keeps away from the hazards caused by sensitive knowledge leakage when sharing the data in their collaborations. This study shows how to protect actionable knowledge for strategic decisions, but at the same time not losing the great benefit of association rule mining. A new algorithm has been proposed to eradicate sensitive knowledge from the released database based on the intersection lattice and impact factor of items in sensitive association rules. The proposed algorithm specifies the victim item such that the alteration of this item causes the slightest impact on the non sensitive frequent association rules. Experimental results demonstrate that our proposed algorithm is appropriate in real context and can achieve significant improvement over other approaches present in the literature. Key–Words: Frequent itemset lattice , Sensitive itemset grouping , Privacy preserving , Hiding association rules.