Synthesize Privacy-Preserving High-Resolution Images via Private Textual Intermediaries
Haoxiang Wang, Zinan Lin, Da Yu, Huishuai Zhang
Generating high-fidelity, differentially private (DP) synthetic images offers a promising route to share and analyze sensitive visual data without compromising individual privacy. However, existing DP image synthesis methods struggle to produce high-resolution outputs that faithfully capture the structure of the original data.  In this paper, we introduce a novel method, referred to as Synthesis via Private Textual Intermediaries (SPTI), that can generate high-resolution images with  strong privacy guarantees and easy adoptions. The key idea is to shift the challenge of DP image synthesis from the image domain to the text domain by leveraging state-of-the-art DP text generation methods. SPTI first summarizes each private image into a concise textual description using image-to-text models, then applies a modified Private Evolution algorithm to generate DP text, and finally reconstructs images using text-to-image models. Notably, SPTI requires no model training, only inferences with off-the-shelf models. Given a private dataset, SPTI produces synthetic images of substantially higher quality than prior DP approaches. On the LSUN Bedroom dataset, SPTI attains an FID $\le$ 26.71 under $\epsilon=1.0$, improving over Private Evolution’s FID of 40.36. Similarly, on MM-CelebA-HQ, SPTI achieves an FID $\le$  33.27 at $\epsilon=1.0$, compared to 57.01 from DP fine-tuning baselines. Overall, our results demonstrate that  Synthesis via Private Textual Intermediaries provides a resource-efficient and proprietary-model-compatible  framework for generating high-resolution DP synthetic images, greatly expanding access to private visual datasets.