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Protecting Sensitive Data in Image Synthesis via PAC-Private Adaptation for Diffusion Models
Synthetic data are increasingly used as an alternative to sharing sensitive records. However, synthetic data generation does not guarantee privacy, as diffusion models trained or adapted on sensitive data remain susceptible to reconstruction attacks. Moreover, while approaches that use differential
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