Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare
This addresses the right to be forgotten for patients in healthcare AI, ensuring compliance with privacy laws, though it appears incremental as it builds on existing concepts of auditing and forgetting.
The paper tackles the problem of revoking patients' private data from pre-trained deep learning models in intelligent healthcare by proposing a unified method that uses auditing to guide forgetting, resulting in the development of AFS, an open-source software that demonstrated generality across four tasks with various datasets and architectures.
Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries. However, with the development of data science, machine learning and deep learning techniques, this right is usually neglected or violated as more and more patients' data are being collected and used for model training, especially in intelligent healthcare, thus making intelligent healthcare a sector where technology must meet the law, regulations, and privacy principles to ensure that the innovation is for the common good. In order to secure patients' right to be forgotten, we proposed a novel solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing a new approach called knowledge purification. To implement our solution, we developed AFS, a unified open-source software, which is able to evaluate and revoke patients' private data from pre-trained deep learning models. We demonstrated the generality of AFS by applying it to four tasks on different datasets with various data sizes and architectures of deep learning networks. The software is publicly available at \url{https://github.com/JoshuaChou2018/AFS}.