Reducing Side Effects of Ant Based Orthogonal Multiplicative and Transformational Algorithm with Sensitive Data Items

Privacy preserving data mining (PPDM) has become a more demanding issue in resolving the effects of I privatizing user's data. Most of the PPDM technique adopting, sensitive item hiding changes the originality of the dataset and were designed to partially evaluate the side effects. Perturbation of sensitive item set is, however, not considered in the evaluation process, thus raising the probability of artificial item sets. In this work, we plan to develop an Optimized Social Ant Based Sensitive Item Hiding (OSA-SIH) technique and expand the scope of quality privacy preservation for distributed data mining with optimal side effects on the original dataset. The sensitive item hiding is performed through multiplicative and transformational data perturbation. This data perturbation is based on socially cohesive relational rate between sensitive and non sensitive item sets, ensuring rate of side effects. The side effects on the modified dataset are checked for several users' requested item set distribution. Experiments are then conducted to show the performance of the proposed technique in rate of side effects.