arXiv Machine Learning
Memorisation bias in medical AI
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arXiv Machine Learning
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Automated screening of privacy risks in medical AI
The paper introduces a lightweight screening tool that evaluates individual patient vulnerability to membership inference attacks across thousands of medical AI models. By measuring attack scores for each patient, the authors found that certain records consistently rank high in risk regardless of training settings, and that stronger weight decay can reduce overall exposure but not the identity of the most at‑risk patients. The tool uses four small models to flag high‑risk patients before a production model is trained, aiming to integrate privacy assessment into clinical AI data preparation.
arXiv Computer Vision
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A Data-Interventional Framework for Auditing Privacy and Fairness in Generative Medical Imaging
arXiv:2609.26623v1 Announce Type: new Abstract: Diffusion-based synthetic data generation offers a promising route for sharing medical imaging data without releasing sensitive patient records. Howeve...
arXiv Computation and Language
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Clinically Grounded Privacy Evaluation of Medical LMs
The paper introduces a clinically grounded privacy evaluation framework for medical language models, assessing leakage across a spectrum of adversarial access levels—from publicly inferable demographics to leaked note fragments. Using this framework on an LM pretrained on 378,000 clinical notes, the authors find that routine encounter metadata leads to high verbatim memorization and significant recovery of sensitive diagnoses (e.g., AUROC 0.91 for abortion, 0.82 for HIV). They also note that exact-match memorization can overstate disclosure, with 36% of memorized tokens being templated documentation, underscoring the risks of training on longitudinal clinical data and offering a reusable evaluation tool.
arXiv AI
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Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications
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arXiv AI
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Auditing evaluation artifacts and data leakage in large language model systems for rare disease diagnosis
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arXiv Machine Learning
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Reducing information dependency does not cause training data privacy. Adversarially non-robust features do
arXiv:2607. 12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks.