期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2013
卷号：10
期号：2
出版社：IJCSI Press
摘要：The problem of secure and fast distributed classification is an important one. The main focus of the paper is on privacy preserving distributed classification rule mining. This research paper addresses the performance analysis of privacy preserving Nave Bayes classifiers for horizontal and vertical partitioned databases. The Nave Bayes classifier is a simple but efficient baseline classifier. We compare the performance of our two proposed privacy preserving Nave Bayes protocols with basic Nave Bayes classifier (NBC). First protocol used Un-trusted Third Party (UTP) for privacy preserving Nave Bayes classifier for horizontally partitioned data and second protocol used secure multiplication protocol for privacy preserving Nave Bayes classifier for vertically partitioned data. The results analysis shows that our protocols execution time is less than the existing NBC execution time since in our protocol, all parties individually calculate their probability or model parameters as an intermediate result and transfer only these intermediate results for further calculations. Accuracy of test data is same because calculated model parameters of training data are same. Our protocols are very easy to follow, understand with minimum efforts, secure and fast.
