mlsanityData to 7 Oct 2026

Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare

arXiv 2302.09813 ↗First posted 20 Feb 2023cs.LG cs.CRcited by 9

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Abstract (arXiv)

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 urlhttps://github.com/JoshuaChou2018/AFS.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 34 of its 61 references that are in the map. First posted on arXiv 20 Feb 2023.

Builds on

34 of 61 references.

References · 34

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22 more
  1. 2021Remember What You Want to Forget: Algorithms for Machine Unlearningcited by 360
  2. 2020Descent-to-Delete: Gradient-Based Methods for Machine Unlearningcited by 303
  3. 2022A Survey of Machine Unlearningcited by 298
  4. 2021Unrolling SGD: Understanding Factors Influencing Machine Unlearningcited by 274
  5. 2020Systematic Evaluation of Privacy Risks of Machine Learning Modelscited by 273
  6. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  7. 2020Approximate Data Deletion from Machine Learning Modelscited by 250
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  9. 2022Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teachercited by 189
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  11. 2019Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inferencecited by 168
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  22. 2021EMA: Auditing Data Removal from Trained Modelscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 3; 2026: 1.

9 papers.

  1. 2024Learning to Unlearn for Robust Machine Unlearningcited by 19
  2. 2023Path to Medical AGI: Unify Domain-specific Medical LLMs with the Lowest Costcited by 12
  3. 2024Exploring Fairness in Educational Data Mining in the Context of the Right to be Forgottencited by 3
  4. 2025Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settingscited by 3
  5. 2024SoK: Security and Privacy Risks of Healthcare AIcited by 2
  6. 2025Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategiescited by 2
  7. 2025Ready2Unlearn: A Learning-Time Approach for Preparing Models with Future Unlearning Readinesscited by 2
  8. 2024From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Modelscited by 1
  9. 2026Revealing Training Data Exposure in Vision Language Large Models via Parameter Gradientscited by 0