Privacy-Preserving Diffusion Model Using Homomorphic Encryption
This addresses privacy concerns for users of generative image models in critical applications, representing an incremental improvement by applying encryption to a specific model.
The paper tackles the problem of privacy in stable diffusion models by introducing HE-Diffusion, a framework using homomorphic encryption to protect the denoising phase, achieving a 500 times speedup and minute-level inference time while maintaining accuracy comparable to plaintext models.
In this paper, we introduce a privacy-preserving stable diffusion framework leveraging homomorphic encryption, called HE-Diffusion, which primarily focuses on protecting the denoising phase of the diffusion process. HE-Diffusion is a tailored encryption framework specifically designed to align with the unique architecture of stable diffusion, ensuring both privacy and functionality. To address the inherent computational challenges, we propose a novel min-distortion method that enables efficient partial image encryption, significantly reducing the overhead without compromising the model's output quality. Furthermore, we adopt a sparse tensor representation to expedite computational operations, enhancing the overall efficiency of the privacy-preserving diffusion process. We successfully implement HE-based privacy-preserving stable diffusion inference. The experimental results show that HE-Diffusion achieves 500 times speedup compared with the baseline method, and reduces time cost of the homomorphically encrypted inference to the minute level. Both the performance and accuracy of the HE-Diffusion are on par with the plaintext counterpart. Our approach marks a significant step towards integrating advanced cryptographic techniques with state-of-the-art generative models, paving the way for privacy-preserving and efficient image generation in critical applications.