Distribution-Level Feature Distancing for Machine Unlearning: Towards a Better Trade-off Between Model Utility and Forgetting

TL;DR

This paper introduces Distribution-Level Feature Distancing (DLFD), a new method for machine unlearning that effectively forgets specific data while maintaining model utility, addressing privacy concerns in deep learning applications.

Contribution

DLFD is a novel approach that synthesizes data to distinguish feature distributions of forget samples, improving unlearning efficiency and utility preservation in a single epoch.

Findings

Outperforms existing unlearning methods in forgetting accuracy

Preserves task-relevant feature correlations effectively

Achieves unlearning within a single training epoch

Abstract

With the explosive growth of deep learning applications and increasing privacy concerns, the right to be forgotten has become a critical requirement in various AI industries. For example, given a facial recognition system, some individuals may wish to remove their personal data that might have been used in the training phase. Unfortunately, deep neural networks sometimes unexpectedly leak personal identities, making this removal challenging. While recent machine unlearning algorithms aim to enable models to forget specific data, we identify an unintended utility drop-correlation collapse-in which the essential correlations between image features and true labels weaken during the forgetting process. To address this challenge, we propose Distribution-Level Feature Distancing (DLFD), a novel method that efficiently forgets instances while preserving task-relevant feature correlations. Our method synthesizes data samples by optimizing the feature distribution to be distinctly different from that of forget samples, achieving effective results within a single training epoch. Through extensive experiments on facial recognition datasets, we demonstrate that our approach significantly outperforms state-of-the-art machine unlearning methods in both forgetting performance and model utility preservation.