  Machine unlearning has great significance in guaranteeing model security and
protecting user privacy. Additionally, many legal provisions clearly stipulate
that users have the right to demand model providers to delete their own data
from training set, that is, the right to be forgotten. The naive way of
unlearning data is to retrain the model without it from scratch, which becomes
extremely time and resource consuming at the modern scale of deep neural
networks. Other unlearning approaches by refactoring model or training data
struggle to gain a balance between overhead and model usability.
  In this paper, we propose an approach, dubbed as DeepObliviate, to implement
machine unlearning efficiently, without modifying the normal training mode. Our
approach improves the original training process by storing intermediate models
on the hard disk. Given a data point to unlearn, we first quantify its temporal
residual memory left in stored models. The influenced models will be retrained
and we decide when to terminate the retraining based on the trend of residual
memory on-the-fly. Last, we stitch an unlearned model by combining the
retrained models and uninfluenced models. We extensively evaluate our approach
on five datasets and deep learning models. Compared to the method of retraining
from scratch, our approach can achieve 99.0%, 95.0%, 91.9%, 96.7%, 74.1%
accuracy rates and 66.7$\times$, 75.0$\times$, 33.3$\times$, 29.4$\times$,
13.7$\times$ speedups on the MNIST, SVHN, CIFAR-10, Purchase, and ImageNet
datasets, respectively. Compared to the state-of-the-art unlearning approach,
we improve 5.8% accuracy, 32.5$\times$ prediction speedup, and reach a
comparable retrain speedup under identical settings on average on these
datasets. Additionally, DeepObliviate can also pass the backdoor-based
unlearning verification.
