Title: Privacy-Aware Hybrid Image Synthesis with Local-Cloud Collaboration
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: generative models, inference scaling, linear attention, privacy protection

Abstract: Recent advances in diffusion-based generative models have enabled high-quality and personalized image synthesis. However, protecting user privacy while enabling efficient image generation remains a major challenge when deploying diffusion models on edge devices. Cloud-based inference risks exposing sensitive content, whereas fully local execution demands excessive computation and memory. This calls for a collaborative edge–cloud paradigm that can balance both concerns. In this work, we propose PrivInfer, a training-free, privacy-preserving inference framework that enables efficient edge-cloud collaboration for image generation. PrivInfer decomposes the generation process by region: privacy-sensitive areas are processed locally, while non-sensitive regions are offloaded to the cloud. This design reduces on-device computation while minimizing privacy risks. To ensure secure cross-device interaction, we introduce a more secured mechanism that shares structural information without exposing raw features. We further develop a ring-based masking strategy to structurally isolate private content during convolution, and a heterogeneous-step scheme that enables low-step local models to leverage high-fidelity cloud features. Extensive experiments show that PrivInfer significantly reduces inference steps and computational load on edge devices, while maintaining high generation fidelity and strong privacy protection, which offers a practical solution for private and efficient diffusion model deployment.