A Privacy Preserving Mining Algorithm on Distributed Dataset
The issue of maintaining privacy in data mining has attracted considerable attention over the last few years. The difficulty lies in the fact that the two metrics for evaluating privacy preserving data mining methods: privacy and accuracy are typically contradictory in nature. This paper addresses privacy preserving mining of association rules on distributed dataset. We present an algorithm, based on a probabilistic approach of distorting transactions in the dataset, which can provide high privacy of individual information and at the same time acquire a high level of accuracy in the mining result. Finally, we present experiment results that validate the algorithm.
KeywordsAssociation Rule Minimum Support Frequent Itemsets Markov Chain Model Transition Probability Matrix
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