  In recent years, machine learning neural network has penetrated deeply into
people's life. As the price of convenience, people's private information also
has the risk of disclosure. The "right to be forgotten" was introduced in a
timely manner, stipulating that individuals have the right to withdraw their
consent from personal information processing activities based on their consent.
To solve this problem, machine unlearning is proposed, which allows the model
to erase all memory of private information. Previous studies, including
retraining and incremental learning to update models, often take up extra
storage space or are difficult to apply to neural networks. Our method only
needs to make a small perturbation of the weight of the target model and make
it iterate in the direction of the model trained with the remaining data subset
until the contribution of the unlearning data to the model is completely
eliminated. In this paper, experiments on five datasets prove the effectiveness
of our method for machine unlearning, and our method is 15 times faster than
retraining.
