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：机器遗忘（MU）算法因必须遵守数据隐私法规而变得日益关键。MU的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，现有方法聚焦于最大化用户隐私保护。然而，现实世界中的各类网络应用对隐私保护程度的要求各不相同。探讨隐私、模型效用与运行效率之间的全面权衡，对于实际遗忘场景至关重要。此外，设计能简单调控上述权衡的MU算法虽具吸引力，但因其固有的复杂交互性而充满挑战。为应对这些挑战，我们提出了可控机器遗忘（ConMU）——一种旨在促进MU校准的新型框架。ConMU框架包含三个核心模块：一个协调运行效率与模型泛化的重要数据选择模块、一个平衡隐私与模型泛化的渐进式高斯机制模块，以及一个控制隐私与运行效率之间权衡的遗忘代理。在多个基准数据集上的全面实验表明，我们的控制机制具有强大的适应性，且优于已有遗忘方法。ConMU探索了隐私-效用-效率权衡的全谱系，使从业者能够针对不同的现实法规进行考量。源代码见：https://github.com/guangyaodou/ConMU。