Privacy-Preserving Fair Learning of Support Vector Machine with Homomorphic Encryption

Abstract

Fair learning has received a lot of attention in recent years since machine learning models can be unfair in automated decision-making systems with respect to sensitive attributes such as gender, race, etc. However, to mitigate the discrimination on the sensitive attributes and train a fair model, most fair learning methods have required to get access to the sensitive attributes in training or validation phases. In this study, we propose a privacy-preserving training algorithm for a fair support vector machine classifier based on Homomorphic Encryption (HE), where the privacy of both sensitive information and model secrecy can be preserved. The expensive computational costs of HE can be significantly improved by protecting only the sensitive information, introducing refined formulation and low-rank approximation using shared eigenvectors. Through experiments on the synthetic and real-world data, we demonstrate the effectiveness of our algorithm in terms of accuracy and fairness and show that our method significantly outperforms other privacy-preserving solutions in terms of better trade-offs between accuracy and fairness. To the best of our knowledge, our algorithm is the first privacy-preserving fair learning algorithm using HE.