An Optimization based Modified Maximum Sensitive Item-Sets Conflict First Algorithm (MSICF) for Hiding Sensitive Item-Sets

In privacy preserving data mining, utility mining plays an important role. In privacy preserving utility mining, some sensitive itemsets are hidden from the database according to certain privacy policies. Hiding sensitive itemsets from the adversaries is becoming an important issue nowadays. The existing paper utilized two algorithms; such as HHUIF and MSICF are conceal the sensitive itemsets, so that the adversaries cannot mine them from the modified database. But, the performance of this method lacks if the utility value of the items are same. To solve this problem, in this paper a Modified MSICF algorithm (MMSICF) is proposed. The proposed MMSICF algorithm is a modified version of existing MSICF algorithm. The MMSICF algorithm computes the sensitive itemsets by utilizing the user defined utility threshold value. The threshold value selection plays a major role in this paper and it is determined by the hybridization of Artificial Bee Colony (ABC) and Genetic Algorithm (GA). In order to hide the sensitive itemsets, the frequency value of the items is changed. The proposed MMSICF reduces the computation complexity as well as improves the hiding performance of the itemsets. The algorithm is implemented and the resultant itemsets are compared against the itemsets that are obtained from the conventional privacy preserving utility mining algorithms. General Terms Data Mining, Privacy

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