We explore user-level gradient inversion as a new attack surface in distributed learning. We first investigate existing attacks on their ability to make inferences about private information beyond training data reconstruction. Motivated by the low reconstruction quality of existing methods, we propose a novel gradient inversion attack that applies a denoising diffusion model as a strong image prior in order to enhance recovery in the large batch setting. Unlike traditional attacks, which aim to reconstruct individual samples and suffer at large batch and image sizes, our approach instead aims to recover a representative image that captures the sensitive shared semantic information corresponding to the underlying user. Our experiments with face images demonstrate the ability of our methods to recover realistic facial images along with private user attributes.

本研究针对分布式学习中的用户级梯度反演作为一种新攻击方式，填补现有方法在重建训练数据之外推断私人信息的能力不足的空白。我们提出了一种新的梯度反演攻击方法，采用去噪扩散模型作为强图像先验，以提高大批量设置下的恢复效果。实验表明，该方法能够有效恢复逼真的面部图像及其私人用户属性，具有重要的潜在影响。

基于扩散先验的用户级梯度反演研究