arXiv Computer Vision
Quantifying privacy risks in foundation model representations of brain MRI
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arXiv Machine Learning
Jul 27
The pretraining domain outweighs the training objective in setting the privacy-utility trade-off of differentially private medical image analysis
arXiv:2601. 19618v2 Announce Type: replace-cross Abstract: Differential privacy protects the patients whose images train medical imaging models, but it lowers diagnostic accuracy, and the initialization is the strongest known remedy.
arXiv Computer Vision
Sep 23
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 Machine Learning
Aug 28
Private and interpretable clinical prediction with quantum-inspired tensor train models
The paper demonstrates that publicly available clinical machine learning models, such as logistic regression (LR), pose significant privacy risks because attackers can recover model parameters and identify training cohorts through various membership inference attacks. The authors show that even small cohorts can be reliably identified and that common practices like cross-validation can worsen the risk. To mitigate this, they propose a quantum-inspired defense that tensorizes discretized models into tensor trains (TTs), which obfuscates parameters, preserves accuracy, and maintains interpretability while providing black‑box protection comparable to Differential Privacy.
arXiv AI
4d ago
Privacy-Preserving Personalized Federated Learning (PPP-FL) for Medical Imaging
arXiv:2610.27470v1 Announce Type: cross Abstract: Federated learning (FL) is a distributed machine learning paradigm that reduces transmission costs, mitigates cross-border data sharing restrictions,...
arXiv Machine Learning
2d ago
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 AI
Jul 15
Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast
arXiv:2603. 04113v2 Announce Type: replace-cross Abstract: Demographic attributes can be predicted from medical images, raising concerns about bias in clinical AI systems.