KNT applies key-conditioned nonlinear obfuscation to split-inference features, cutting re-identification AUC from 0.635 to 0.586 with 0.15 ms overhead and under 1 pp accuracy loss.
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MediSyn is a generalist latent diffusion model that synthesizes text-guided medical images across multiple specialties and modalities from public data and improves downstream classifiers in low-data settings.
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Keyed Nonlinear Transform: Lightweight Privacy-Enhancing Feature Sharing for Medical Image Analysis
KNT applies key-conditioned nonlinear obfuscation to split-inference features, cutting re-identification AUC from 0.635 to 0.586 with 0.15 ms overhead and under 1 pp accuracy loss.
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A Generalist Model for Diverse Text-Guided Medical Image Synthesis
MediSyn is a generalist latent diffusion model that synthesizes text-guided medical images across multiple specialties and modalities from public data and improves downstream classifiers in low-data settings.