Secure Naïve Bayes Classification without Loss of Accuracy with Application to Breast Cancer Prediction

Abstract

The classification and prediction accuracy of Machine Learning (ML) algorithms, which often outperform human experts of the related field, have enabled them to be used in areas such as health and disease prediction, image and speech recognition, cyber-security threats and credit-card fraud detection and others. However, laws, ethics and privacy concerns prevent ML algorithms to be used in many real-case scenarios. In order to overcome this problem, we introduce a few flexible and secure building blocks which can be used to build different privacy preserving classifications schemes based on already trained ML models. Then, as a use-case scenario, we utilize and practically use those blocks to enable a privacy preserving Naïve Bayes classifier in the semi-honest model with application to breast cancer detection. Our theoretical analysis and experimental results show that the proposed scheme in many aspects is more efficient in terms of computation and communication cost, as well as in terms of security properties than several state of the art schemes. Furthermore, our privacy preserving scheme shows no loss of accuracy compared to the plain classifier.