Krill Based Optimal High Utility Item Selector (OHUIS) for Privacy Preserving Hiding Maximum Utility Item Sets
Ketthari Thandapani, Rajendran Sugumar · International journal of intelligent engineering and systems · 2017
Privacy Preserving Data Mining (PPDM) has turned into a well-known research region.Step by step instructions are adjusted between privacy assurance and knowledge discovery in the sharing procedure is a critical issue.Likewise, currently, not more strategies are accessible in the literature to hide the sensitive itemsets in the database.One of the existing privacy-preserving utility mining strategies uses two algorithms, HHUIF and MSICF to disguise the sensitive item sets so that the foes can't mine them from the modified database.This paper concentrates the privacy for the sensitive data actualize in a hybrid approach that is Optimal High Utility Item Selector (OHUIS) method.This Item Selector (IS) procedure upgrades the threshold utilizing inspired Krill Herd Optimization (KHO) method.With a specific end goal to hide the sensitive item sets, the frequency estimation of the items is changed.On the off chance that the utility estimations of the items are same, the OHUIS algorithm chooses the precise items and after that, the frequency estimations of the selected items are altered.The proposed OHUIS reduces the computation many-sided quality and also enhances the hiding performance of the item sets.The algorithm is actualized and the resultant item sets are thought about against the item sets that are acquired from the traditional privacy preserving utility mining algorithms.The test comes about demonstrate that OHUIS accomplishes the lower miss costs compared to the existing procedures for various manufactured datasets.