Unlearning Personal Data from a Single Image

TL;DR

This paper introduces a new benchmark and method for unlearning personal identity data from models when training data is unavailable, using only a single image for effective identity removal.

Contribution

The paper proposes 1-SHUI, a novel benchmark for one-shot unlearning without training data, and introduces MetaUnlearn, a meta-learning approach to forget identities from a single image.

Findings

Existing methods struggle with limited data availability.

Performance drops when test samples differ from training data.

MetaUnlearn outperforms baseline approaches in the proposed setting.

Abstract

Machine unlearning aims to erase data from a model as if the latter never saw them during training. While existing approaches unlearn information from complete or partial access to the training data, this access can be limited over time due to privacy regulations. Currently, no setting or benchmark exists to probe the effectiveness of unlearning methods in such scenarios. To fill this gap, we propose a novel task we call One-Shot Unlearning of Personal Identities (1-SHUI) that evaluates unlearning models when the training data is not available. We focus on unlearning identity data, which is specifically relevant due to current regulations requiring personal data deletion after training. To cope with data absence, we expect users to provide a portraiting picture to aid unlearning. We design requests on CelebA, CelebA-HQ, and MUFAC with different unlearning set sizes to evaluate applicable methods in 1-SHUI. Moreover, we propose MetaUnlearn, an effective method that meta-learns to forget identities from a single image. Our findings indicate that existing approaches struggle when data availability is limited, especially when there is a dissimilarity between the provided samples and the training data. Source code available at https://github.com/tdemin16/one-shui.