Differentially Private Naive Bayes Classification

Privacy and security concerns often prevent the sharing of users´ data or even of the knowledge gained from it, thus deterring valuable information from being utilized. Privacy-preserving knowledge discovery, if done correctly, can alleviate this problem. One of the most important and widely used data mining techniques is that of classification. We consider the model where a single provider has centralized access to a dataset and would like to release a classifier while protecting privacy to the best extent possible. Recently, the model of differential privacy has been developed which provides a strong privacy guarantee even if adversaries hold arbitrary prior knowledge. In this paper, we apply this rigorous privacy model to develop a Naive Bayes classifier, which is often used as a baseline and consistently provides reasonable classification performance. We experimentally evaluate the proposed approach, and discuss how it could be potentially deployed in PaaS clouds.