Learn to Unlearn: A Survey on Machine Unlearning

• URL: http://arxiv.org/abs/2305.07512v2
• Date: Thu, 26 Oct 2023 23:21:18 GMT
• Title: Learn to Unlearn: A Survey on Machine Unlearning
• Authors: Youyang Qu, Xin Yuan, Ming Ding, Wei Ni, Thierry Rakotoarivelo, David Smith
• Abstract summary: This article presents a review of recent machine unlearning techniques, verification mechanisms, and potential attacks. We highlight emerging challenges and prospective research directions. We aim for this paper to provide valuable resources for integrating privacy, equity, andresilience into ML systems.
• Score: 29.077334665555316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine Learning (ML) models have been shown to potentially leak sensitive information, thus raising privacy concerns in ML-driven applications. This inspired recent research on removing the influence of specific data samples from a trained ML model. Such efficient removal would enable ML to comply with the "right to be forgotten" in many legislation, and could also address performance bottlenecks from low-quality or poisonous samples. In that context, machine unlearning methods have been proposed to erase the contributions of designated data samples on models, as an alternative to the often impracticable approach of retraining models from scratch. This article presents a comprehensive review of recent machine unlearning techniques, verification mechanisms, and potential attacks. We further highlight emerging challenges and prospective research directions (e.g. resilience and fairness concerns). We aim for this paper to provide valuable resources for integrating privacy, equity, andresilience into ML systems and help them "learn to unlearn".

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