  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.
