Machine unlearning has become a pivotal task to erase the influence of data
from a trained model. It adheres to recent data regulation standards and
enhances the privacy and security of machine learning applications. Most
existing machine unlearning methods perform well, however, they typically
necessitate access to the entirety of the remaining data, which might not be
feasible in certain scenarios. In this work, we present a new machine
unlearning approach Scissorhands, which operates effectively with only a subset
of the training data. Initially, Scissorhands identifies the most pertinent
parameters in the given model relative to the forgetting data via connection
sensitivity. This process involves reinitializing the most influential top-$k$
percent of these parameters, resulting in a trimmed model for erasing the
influence of the forgetting data. Subsequently, Scissorhands retrains the
trimmed model through a min-max optimization process, seeking parameters that
preserve information on the remaining data while discarding information related
to the forgetting data. Our experimental results, conducted across five
distinct datasets and utilizing both CNN and ViT, demonstrate that
Scissorhands, despite utilizing only a limited portion of the training data,
showcases competitive performance when compared to existing methods.

机器遗忘是一项关键任务，用于消除训练模型的数据影响，遵循最新的数据监管标准，增强机器学习应用的隐私和安全。本研究提出了一种名为 Scissorhands 的新型机器遗忘方法，通过连接敏感性识别与遗忘数据相关的最相关参数，重新初始化这些参数的前 k% 并通过最小最大优化过程重新训练模型，这样可以在只使用有限训练数据的情况下展示与现有方法相比的竞争性能。