SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles

TL;DR

This paper introduces SF-PATE, a scalable framework that combines privacy and fairness in model training, enabling the use of off-the-shelf fair models for large neural networks without compromising individual privacy.

Contribution

It presents a novel scalable method for training private and fair models simultaneously, addressing the challenge of non-disclosed sensitive attributes.

Findings

Achieves a balance between accuracy, privacy, and fairness.

Enables training of large neural networks with privacy and fairness guarantees.

Demonstrates effectiveness on multiple prediction tasks.

Abstract

A critical concern in data-driven processes is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of the group attributes is essential. However, in practice, these attributes may not be available due to legal and ethical requirements. To address this challenge, this paper studies a model that protects the privacy of the individuals' sensitive information while also allowing it to learn non-discriminatory predictors. A key characteristic of the proposed model is to enable the adoption of off-the-selves and non-private fair models to create a privacy-preserving and fair model. The paper analyzes the relation between accuracy, privacy, and fairness, and the experimental evaluation illustrates the benefits of the proposed models on several prediction tasks. In particular, this proposal is the first to allow both scalable and accurate training of private and fair models for very large neural networks.