  Machine learning models, especially deep models, may unintentionally remember
information about their training data. Malicious attackers can thus pilfer some
property about training data by attacking the model via membership inference
attack or model inversion attack. Some regulations, such as the EU's GDPR, have
enacted "The Right to Be Forgotten" to protect users' data privacy, enhancing
individuals' sovereignty over their data. Therefore, removing training data
information from a trained model has become a critical issue. In this paper, we
present a GAN-based algorithm to delete data in deep models, which
significantly improves deleting speed compared to retraining from scratch,
especially in complicated scenarios. We have experimented on five commonly used
datasets, and the experimental results show the efficiency of our method.
