Information Removal at the bottleneck in Deep Neural Networks

TL;DR

This paper introduces IRENE, a method to explicitly remove specific private information from deep neural network representations by minimizing mutual information, addressing privacy and bias concerns.

Contribution

The paper proposes IRENE, a novel approach for information removal at the bottleneck of neural networks through mutual information minimization, enhancing privacy and fairness.

Findings

Effective information removal demonstrated on synthetic data

Validated approach on CelebA dataset

Opens pathways for privacy-preserving neural network design

Abstract

Deep learning models are nowadays broadly deployed to solve an incredibly large variety of tasks. Commonly, leveraging over the availability of "big data", deep neural networks are trained as black-boxes, minimizing an objective function at its output. This however does not allow control over the propagation of some specific features through the model, like gender or race, for solving some an uncorrelated task. This raises issues either in the privacy domain (considering the propagation of unwanted information) and of bias (considering that these features are potentially used to solve the given task). In this work we propose IRENE, a method to achieve information removal at the bottleneck of deep neural networks, which explicitly minimizes the estimated mutual information between the features to be kept ``private'' and the target. Experiments on a synthetic dataset and on CelebA validate the effectiveness of the proposed approach, and open the road towards the development of approaches guaranteeing information removal in deep neural networks.