Developing Adaptive Homomorphic Encryption through Exploration of Differential Privacy

Abstract

Machine Learning (ML) classifiers are pivotal in various applied ML domains. The accuracy of these classifiers requires meticulous training, making the exposure of training datasets a critical concern, especially concerning privacy. This study identifies a significant trade-off between accuracy, computational efficiency, and security of the classifiers. Integrating classical Homomorphic Encryption (HE) and Differential Privacy (DP) highlights the challenges in parameter tuning inherent to such hybrid methodologies. These challenges concern the analytical components of the HE algorithm’s privacy budget and simultaneously affect the sensitivity to noise in the subjected ML hybrid classifiers.

This paper explores these areas and proposes a hybrid model using a basic client-server architecture to combine HE and DP algorithms. It then examines the sensitivity analysis of the aforementioned trade-off features. Additionally, the paper outlines initial observations after deploying the proposed algorithm, contributing to the ongoing discourse on optimizing the balance between accuracy, computational efficiency, and security in ML classifiers.