Privacy-preserving Naive Bayesian Classification

Z. Zhan, L.W. Chang (USA), and S. Matwin (Canada)


Privacy, security, naive Bayesian classification, data mining


Privacy is an important issue in data mining and knowledge discovery. In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specifically, we present a method to build naive Bayesian classifiers from the disguised data. We conduct experiments to compare the accuracy of our classifier with the one built from the original undisguised data. Our results show that although the data are disguised, our method can still achieve fairly high accuracy.

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