Association rule hiding an important issue in recent years due to the development of privacy preserving data mining techniques for hiding the association rules. One of the mostly used techniques to hide association rules is the sanitization of the database. In this paper, a novel algorithm MPSO2DT has been proposed based on the Particle Swarm Optimization (PSO) in order to reduce the side effects. The aim is to reduce the side effects such as Sensitive item-set hiding failure, Non-sensitive misses, extra item-set generations and Database dissimilarities along with the reduction of running time and complexities through transaction deletion.

In recent day’s huge rapid growth of corporate industries professional are based on the online marketing. These markets are associated with millions of online transactions which contain the details of the items, number of items, price and additional information like working details, salary information and personal information. The customers associated with these transactions are concerned about privacy issues. This manuscript aims to concentrates more on the additional information about the customer apart from dealing with the items. More analysis helps in knowing the sensitive information about an individual. In this article two algorithms were used, out of which first algorithm has been used to hide the sensitive information about an individual and other proposed algorithm has been used to hide the sensitive transaction information. These algorithms are proposed based on k-Anonymity and association rule hiding techniques. A novel algorithm has been proposed for association rule hiding algorithm to reduce the side effects such as Sensitive item-set hiding failure, Non-sensitive misses, extra item-set generations and Database dissimilarities along with the reduction of running time and complexities through transaction deletion.

Current days privacy concern about an individual, an organization and social media etc. plays a vital role. Online business deals with millions of transactions daily, these transactions may leads to privacy issues. Association rule hiding is a solution to these privacy issue, which focuses on hiding the sensitive information produces from online departmental stores ,face book datasets etc..These techniques are used to identify the sensitive rules and provide the privacy to the sensitive rules, so that results the lost rules and ghost rules. Algorithms developed so far are lack in achieving the better outcomes. This paper propose two novel algorithm that uses the properties from genetic algorithm and water marking algorithm for better way of hiding the sensitive association rules.