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

Abstract

The problem of secure distributed classification is an im-portant one. In many situations, data is split between multi-ple organizations. These organizations may want to utilize all of the data to create more accurate predictive models while revealing neither their training data / databases nor the instances to be classified. The Naive Bayes Classifier is a simple but efficient baseline classifier. In this paper, we present a privacy preserving Naive Bayes Classifier for horizontally partitioned data.