Recently, serious concerns have been raised about the privacy issues related to training datasets in machine learning algorithms when including personal data. Various regulations in different countries, including the GDPR grant individuals to have personal data erased, known as 'the right to be forgotten' or 'the right to erasure'. However, there has been less research on effectively and practically deleting the requested personal data from the training set while not jeopardizing the overall machine learning performance. In this work, we propose a fast and novel machine unlearning paradigm at the layer level called layer attack unlearning, which is highly accurate and fast compared to existing machine unlearning algorithms. We introduce the Partial-PGD algorithm to locate the samples to forget efficiently. In addition, we only use the last layer of the model inspired by the Forward-Forward algorithm for unlearning process. Lastly, we use Knowledge Distillation (KD) to reliably learn the decision boundaries from the teacher using soft label information to improve accuracy performance. We conducted extensive experiments with SOTA machine unlearning models and demonstrated the effectiveness of our approach for accuracy and end-to-end unlearning performance.

该研究提出了一种在机器学习中，通过层攻击遗忘和知识蒸馏来解决训练集中的个人数据隐私问题的快速机器遗忘范式，通过大量实验证明了该方法在准确性和全链路遗忘性能上的有效性。

层级攻击遗忘：通过层级攻击和知识蒸馏实现快速准确的机器遗忘