We investigate the construction of gradient-guided conditional diffusion models for reconstructing private images, focusing on the adversarial interplay between differential privacy noise and the denoising capabilities of diffusion models. While current gradient-based reconstruction methods struggle with high-resolution images due to computational complexity and prior knowledge requirements, we propose two novel methods that require minimal modifications to the diffusion model's generation process and eliminate the need for prior knowledge. Our approach leverages the strong image generation capabilities of diffusion models to reconstruct private images starting from randomly generated noise, even when a small amount of differentially private noise has been added to the gradients. We also conduct a comprehensive theoretical analysis of the impact of differential privacy noise on the quality of reconstructed images, revealing the relationship among noise magnitude, the architecture of attacked models, and the attacker's reconstruction capability. Additionally, extensive experiments validate the effectiveness of our proposed methods and the accuracy of our theoretical findings, suggesting new directions for privacy risk auditing using conditional diffusion models.

本研究针对当前梯度重建方法在高分辨率图像重建中存在的计算复杂性和先验知识需求问题，提出了两种创新方法，最小化对扩散模型生成过程的修改，无需先验知识。研究还系统分析了差分隐私噪声对重建图像质量的影响，并通过广泛实验验证了所提方法的有效性，开辟了使用条件扩散模型进行隐私风险审计的新方向。

梯度引导的条件扩散模型用于私密图像重建：分析差分隐私和去噪的对抗性影响