Modern privacy regulations have spurred the evolution of machine unlearning, a technique that enables the removal of data from an already trained ML model without requiring retraining from scratch. Previous unlearning methods tend to induce the model to achieve lowest classification accuracy on the removal data. Nonetheless, the authentic objective of machine unlearning is to align the unlearned model with the gold model, i.e., achieving the same classification accuracy as the gold model. For this purpose, we present a Twin Machine Unlearning (TMU) approach, where a twin unlearning problem is defined corresponding to the original unlearning problem. As a results, the generalization-label predictor trained on the twin problem can be transferred to the original problem, facilitating aligned data removal. Comprehensive empirical experiments illustrate that our approach significantly enhances the alignment between the unlearned model and the gold model. Meanwhile, our method allows data removal without compromising the model accuracy.

翻译：现代隐私法规推动了机器遗忘技术的发展，该技术使得无需从头开始重新训练即可从已训练的机器学习模型中移除数据。以往的遗忘方法倾向于引导模型在移除数据上达到最低的分类准确率。然而，机器遗忘的真实目标在于使遗忘后的模型与黄金模型对齐，即达到与黄金模型相同的分类准确率。为此，我们提出了一种双生机器遗忘方法，该方法针对原始遗忘问题定义了一个对应的双生遗忘问题。由此，在双生问题上训练得到的泛化标签预测器可以迁移至原始问题，从而促进对齐的数据移除。全面的实证实验表明，我们的方法显著增强了遗忘模型与黄金模型之间的对齐程度。同时，该方法能够在移除数据的同时不损害模型的准确率。