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Pretrained representations are increasingly used to share and reuse information from medical images without exposing the original scans, but it is unclear how much patient-specific information these representations retain. We investigate this question for brain magnetic resonance imaging (MRI) by evaluating five frozen...
It is found that task capability and privacy leakage are fundamentally coupled: more capable models are systematically leakier, with no model in the evaluation able to decouple the two.
PrivacySkills is introduced, a controlled framework for evaluating how agents choose among acquisition pathways that provide the same task-relevant value: consulting publicly available personal information, accessing confidential sources, or interacting with the user.
It is demonstrated that while DP alone can degrade both utility and fairness, applying fairness interventions can partially restore equitable outcomes, and post-processing methods tend to provide more stable fairness–utility trade-offs across privacy budgets and synthesizers.
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