Fig. 1: AFS is a unified method to revoke patients’ private data in intelligent healthcare.

The left side of the figure illustrates the high-level iterative flow of AFS, while the right side illustrates the details of how forgetting and auditing work together. As shown on the left side, given a pre-trained DL model and a query dataset (patients’ private data), AFS could audit and provide confidence whether the query dataset has been used to train the target DL model. When a dataset has been used to train the target DL model, AFS could effectively forget the information about the dataset from the target DL model with the guidance of auditing. To achieve that, we proposed a method called knowledge purification as shown on the right side, which utilizes results from auditing as a new term in the loss function to forget information. The brain icon is Designed by macrovector/Freepik.