审计以遗忘：一种在智能医疗中撤销患者隐私数据的统一方法
Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare
AI总结：
本文提出审计引导遗忘与知识纯化方法，并开发开源软件AFS，统一实现智能医疗中患者隐私数据的使用审计与从预训练模型中的撤销，验证了其通用性。
摘要：
撤销个人隐私数据是基本人权之一，许多国家的多部隐私保护法律已对这一权利予以保障。然而，随着数据科学、机器学习与深度学习技术的发展，这一权利往往被忽视或侵犯，因为越来越多的患者数据被收集并用于模型训练，尤其是在智能医疗领域，这使得智能医疗成为一个技术必须与法律、法规和隐私原则相契合的领域，以确保创新服务于公共利益。为保障患者的被遗忘权，我们提出了一种新颖的解决方案，即利用审计来引导遗忘过程，其中审计是指判断某个数据集是否已被用于训练模型，而遗忘则要求将某个查询数据集的信息从目标模型中移除。我们通过引入一种名为知识纯化的新方法，将这两项任务统一起来。为实施该方案，我们开发了AFS，一个统一的开源软件，能够评估并从预训练深度学习模型中撤销患者隐私数据。我们通过将AFS应用于不同数据集上的四项任务，涵盖不同数据规模和深度学习网络架构，证明了AFS的通用性。该软件已公开发布于\url{https://github.com/JoshuaChou2018/AFS}。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
