arXiv:2405.20380v1 Announce Type: new
Abstract: Diffusion models are becoming defector generative models, which generate exceptionally high-resolution image data. Training effective diffusion models require massive real data, which is privately owned by distributed parties. Each data party can collaboratively train diffusion models in a federated learning manner by sharing gradients instead of the raw data. In this paper, we study the privacy leakage risk of gradient inversion attacks. First, we design a two-phase fusion optimization, GIDM, to leverage the well-trained generative model itself as prior knowledge to constrain the inversion search (latent) space, followed by pixel-wise fine-tuning. GIDM is shown to be able to reconstruct images almost identical to the original ones. Considering a more privacy-preserving training scenario, we then argue that locally initialized private training noise $epsilon$ and sampling step t may raise additional challenges for the inversion attack. To solve this, we propose a triple-optimization GIDM+ that coordinates the optimization of the unknown data, $epsilon$ and $t$. Our extensive evaluation results demonstrate the vulnerability of sharing gradient for data protection of diffusion models, even high-resolution images can be reconstructed with high quality.
Analysis of the Content
In this article, the authors discuss the privacy leakage risk of gradient inversion attacks in the context of training diffusion models. Diffusion models are evolving into highly effective generative models that can generate high-resolution image data. However, training these models requires a large amount of real data, which is typically privately owned by distributed parties. To address this, the authors propose a federated learning approach where each data party shares gradients instead of raw data to collaboratively train the diffusion models.
The authors introduce a two-phase fusion optimization method called GIDM (Gradient Inversion Defense Mechanism) to mitigate the privacy leakage risk. GIDM leverages the well-trained generative model itself as prior knowledge to constrain the inversion search space and then performs pixel-wise fine-tuning. The results show that GIDM is able to reconstruct images that are almost identical to the original ones.
Next, the authors consider a more privacy-preserving training scenario and argue that locally initialized private training noise (denoted as $epsilon$) and sampling step (denoted as t) may introduce additional challenges for the inversion attack. To address this, they propose a triple-optimization method called GIDM+ that coordinates the optimization of the unknown data, $epsilon$, and t. The evaluation results demonstrate the vulnerability of sharing gradients for data protection of diffusion models, as high-resolution images can be reconstructed with high quality.
Expert Insights and Multi-disciplinary Nature
This article touches upon several aspects that require multi-disciplinary expertise. The concept of diffusion models as generative models highlights the advancements in the field of computer vision and machine learning. The authors discuss the challenges of training these models using privately owned data and propose a federated learning approach as a solution. This involves the intersection of privacy, distributed computing, and machine learning.
The authors also introduce the concept of gradient inversion attacks and the privacy leakage risks associated with them. This brings in the domain of cybersecurity and adversarial attacks. By analyzing the vulnerabilities and proposing defense mechanisms such as GIDM and GIDM+, the authors contribute to the field of privacy-preserving machine learning and data protection.
The evaluation results presented in the article demonstrate the practical implications of the privacy leakage risks. The ability to reconstruct high-resolution images from shared gradients raises concerns about the privacy of sensitive data. This has implications not only in the field of machine learning but also in domains where privacy is of utmost importance, such as healthcare and finance.
In conclusion, this article highlights the multi-disciplinary nature of the concepts discussed, ranging from computer vision and machine learning to cybersecurity and privacy. The findings and proposed defense mechanisms provide valuable insights for researchers and practitioners working in these fields.