CryptoCredit: Securely Training Fair Models

TL;DR

This paper introduces CryptoCredit, a method that uses homomorphic encryption to enable bias testing in models without revealing sensitive features, ensuring privacy and fairness in regulated decision-making.

Contribution

It presents a novel application of homomorphic encryption for bias testing in linear and logistic regression models, maintaining privacy of sensitive data.

Findings

Practical implementation on adult income dataset

Effective bias testing without revealing sensitive features

Supports linear and logistic regression models

Abstract

When developing models for regulated decision making, sensitive features like age, race and gender cannot be used and must be obscured from model developers to prevent bias. However, the remaining features still need to be tested for correlation with sensitive features, which can only be done with the knowledge of those features. We resolve this dilemma using a fully homomorphic encryption scheme, allowing model developers to train linear regression and logistic regression models and test them for possible bias without ever revealing the sensitive features in the clear. We demonstrate how it can be applied to leave-one-out regression testing, and show using the adult income data set that our method is practical to run.