Machine Unlearning (MU) algorithms have become increasingly critical due to the imperative adherence to data privacy regulations. The primary objective of MU is to erase the influence of specific data samples on a given model without the need to retrain it from scratch. Accordingly, existing methods focus on maximizing user privacy protection. However, there are different degrees of privacy regulations for each real-world web-based application. Exploring the full spectrum of trade-offs between privacy, model utility, and runtime efficiency is critical for practical unlearning scenarios. Furthermore, designing the MU algorithm with simple control of the aforementioned trade-off is desirable but challenging due to the inherent complex interaction. To address the challenges, we present Controllable Machine Unlearning (ConMU), a novel framework designed to facilitate the calibration of MU. The ConMU framework contains three integral modules: an important data selection module that reconciles the runtime efficiency and model generalization, a progressive Gaussian mechanism module that balances privacy and model generalization, and an unlearning proxy that controls the trade-offs between privacy and runtime efficiency. Comprehensive experiments on various benchmark datasets have demonstrated the robust adaptability of our control mechanism and its superiority over established unlearning methods. ConMU explores the full spectrum of the Privacy-Utility-Efficiency trade-off and allows practitioners to account for different real-world regulations. Source code available at: https://github.com/guangyaodou/ConMU.

翻译：机器忘却算法因须严格遵守数据隐私法规而变得日益关键。其主要目标是在无需从头重新训练模型的前提下，擦除特定数据样本对给定模型的影响。为此，现有方法专注于最大化用户隐私保护。然而，现实世界中每个基于网络的应用对隐私有着不同程度的法规要求。充分探索隐私、模型效用与运行时效率之间的全谱系权衡，对于实际忘却场景至关重要。此外，设计能够简单控制上述权衡的机器忘却算法虽令人期待，但因固有的复杂交互而极具挑战性。为应对这些挑战，我们提出了可控机器忘却框架——一个旨在促进机器忘却校准的新型框架。该框架包含三个核心模块：一个调和运行时效率与模型泛化能力的重要数据选择模块，一个平衡隐私与模型泛化能力的渐进式高斯机制模块，以及一个控制隐私与运行时效率之间权衡的忘却代理。在多个基准数据集上的全面实验表明，我们控制机制具有稳健的适应性，且优于已有的忘却方法。ConMU 探索了隐私-效用-效率权衡的全谱系，使从业者能够适应不同的现实世界法规。源代码可在 https://github.com/guangyaodou/ConMU 获取。