Privacy Preserving Naive Bayes Classifier for Horizontally Partitioned Data Using Secure Division

In order to extract interesting patterns, data available at multiple sites has to be trained. The data available in these sites should not be revealed while extorting patterns. Distributed Data mining enables sites to mine patterns based on the knowledge available at different sites. In the process of sites collaborating to develop a model, it is extremely important to protect the privacy of data or intermediate results. The features of the data maintained at each site are often similar in nature. In this paper, we design an improved privacypreserving distributed naive Bayesian classifier to train the horizontal data. This trained model is propagated to sites involved in computation to assist classify a new tuple. We further analyze the security and complexity of the algorithm.

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