Incorporating Privacy Concerns in Data Mining on Distributed Data
Data mining, with its objective to efficiently discover valuable and inherent information from large databases, is particularly sensitive to misuse. Therefore an interesting new direction for data mining research is the development of techniques that incorporate privacy concerns and to develop accurate models without access to precise information in individual data records. 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. We address privacy preserving mining on distributed data in this paper and present an algorithm, based on the combination of probabilistic approach and cryptographic approach, to protect high privacy of individual information and at the same time acquire a high level of accuracy in the mining result.
KeywordsAssociation Rule Minimum Support Frequent Itemsets Markov Chain Model Transition Probability Matrix
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