DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning

Jonathan Lebensold,Maziar Sanjabi,Pietro Astolfi, Adriana Romero-Soriano,Kamalika Chaudhuri, Mike Rabbat,Chuan Guo


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Text-to-image diffusion models have been shown to suffer from sample-level memorization, possibly reproducing near-perfect replica of images that they are trained on, which may be undesirable. To remedy this issue, we develop the first differentially private (DP) retrieval-augmented generation algorithm that is capable of generating high-quality image samples while providing provable privacy guarantees. Specifically, we assume access to a text-to-image diffusion model trained on a small amount of public data, and design a DP retrieval mechanism to augment the text prompt with samples retrieved from a private retrieval dataset. Our differentially private retrieval-augmented diffusion model (DP-RDM) requires no fine-tuning on the retrieval dataset to adapt to another domain, and can use state-of-the-art generative models to generate high-quality image samples while satisfying rigorous DP guarantees. For instance, when evaluated on MS-COCO, our DP-RDM can generate samples with a privacy budget of ϵ=10, while providing a 3.5 point improvement in FID compared to public-only retrieval for up to 10,000 queries.
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