  Machine unlearning techniques, which involve retracting data records and
reducing influence of said data on trained models, help with the user privacy
protection objective but incur significant computational costs. Weight
perturbation-based unlearning is a general approach, but it typically involves
globally modifying the parameters. We propose fine-grained Top-K and Random-k
parameters perturbed inexact machine unlearning strategies that address the
privacy needs while keeping the computational costs tractable.
  In order to demonstrate the efficacy of our strategies we also tackle the
challenge of evaluating the effectiveness of machine unlearning by considering
the model's generalization performance across both unlearning and remaining
data. To better assess the unlearning effect and model generalization, we
propose novel metrics, namely, the forgetting rate and memory retention rate.
However, for inexact machine unlearning, current metrics are inadequate in
quantifying the degree of forgetting that occurs after unlearning strategies
are applied. To address this, we introduce SPD-GAN, which subtly perturbs the
distribution of data targeted for unlearning. Then, we evaluate the degree of
unlearning by measuring the performance difference of the models on the
perturbed unlearning data before and after the unlearning process. By
implementing these innovative techniques and metrics, we achieve
computationally efficacious privacy protection in machine learning applications
without significant sacrifice of model performance. Furthermore, this approach
provides a novel method for evaluating the degree of unlearning.
