Improved Cuckoo Optimization Algorithm for Association Rule Hiding

Abstract

Association Rule Mining (ARM) is a standard data mining practice used to determine interactions hidden in huge sets. Association Rule Hiding (ARH) methods are used to preserve the privacy of data in ARM. ARH process modifies the original database without changing any non-sensitive rules and data. In order to hide the sensitive rules, cuckoo search optimization algorithm that was developed for hiding the sensitive association rules (COA4ARH) was proposed for sensitive rule hiding. In COA4ARH, number of transactions that should be modified to hide the sensitive rules is not considered which may leads to more number of iteration. In this paper, two properties are introduced to select less number of transactions to be modified. It makes the COA4ARH algorithm faster, decreases the number of lost rules and is suitable for variety of datasets. In order to increase the rule hiding capability of COA4ARH, new fitness functions are introduced. The new fitness functions reduce the amount of lost rules and avoid generation of ghost rules which are formed as objectives of COA4ARH algorithm. The multiple objectives in COA4ARH are conflicting with each other. This is known as multi-objective optimization problem. The multi-objective optimization deals with set of non-dominated solutions (Pareto front) for the problem having more than one objective. It is solved by using Crowding Distance (CD) which selects the optimal set of solution for association rule hiding. Thus, the proposed Improved COA4ARH- CD (ICOA4ARH-CD) can be suitable for variety of datasets and effectively hides the sensitive rules with fewer side effects.