  The rise of the phenomenon of the "right to be forgotten" has prompted
research on machine unlearning, which grants data owners the right to actively
withdraw data that has been used for model training, and requires the
elimination of the contribution of that data to the model. A simple method to
achieve this is to use the remaining data to retrain the model, but this is not
acceptable for other data owners who continue to participate in training.
Existing machine unlearning methods have been found to be ineffective in
quickly removing knowledge from deep learning models. This paper proposes using
a stochastic network as a teacher to expedite the mitigation of the influence
caused by forgotten data on the model. We performed experiments on three
datasets, and the findings demonstrate that our approach can efficiently
mitigate the influence of target data on the model within a single epoch. This
allows for one-time erasure and reconstruction of the model, and the
reconstruction model achieves the same performance as the retrained model.
