A New GA Approach Based on Pareto Ranking Strategy for Privacy Preserving of Association Rule Mining

With fast progress of the networks, data mining and information sharing techniques, the security of the privacy of sensitive information in a database becomes a vital issue to be resolved. The mission of association rule mining is discovering hidden relationships between items in database and revealing frequent itemsets and strong association rules. Some rules or frequent itemsets called sensitive which contains some critical information that is vital or private for its owner. In this paper, we investigate the problem of hiding sensitive knowledge. We decide to hide sensitive knowledge both in frequent patterns and association rule extraction steps. In order to conceal association rules and save the utility of transactions in dataset, we select Genetic Algorithm to find optimum state of modification. In our approach various hiding styles are applied in different multi-objective fitness functions. First objective of these functions is hiding sensitive rules and the second one is keeping the accuracy of transactions in dataset. After sanitization process we test the sanitization performance by evaluation of various criterions. Indeed our novel framework consists of dataset preprocessing, Genetic Algorithm-based core approach and different sanitizing measurements. Finally we establish some experiments and test our approach by larger datasets and compare the performance with well-known existing ones.