Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data

Abstract

The problem of secure distributed classification is an important one. In many situations, data is split between multiple 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.