A new approach for sensitive association rules hiding. (30th November 2009) - Dehkordi, Mohammad Naderi; Badie, Kambiz; Zadeh, Ahmad Khadem
Privacy preserving association rule mining has been an active research area since recently. So data mining techniques have been developed in many applications. On the other hand, it also causes an intimidation to privacy of sensitive information. We investigate to find an appropriate balance between an urgency of need for privacy and information extraction on association rules. In this paper, we propose a novel technique for concealing sensitive association rules. In our approach, we do some perturbation in original dataset based on genetic algorithms with suitable fitness function to hide sensitive rules and minimum number of modifications. Finally, a set of experiments is performed to show the effectiveness of our approach.