Adaptive Diffusion Freezing: Privacy-preserving Diffusion Models Against Membership Inference Attacks
Diffusion models have achieved remarkable success in generative tasks across various areas, however their training process raises significant privacy concerns, particularly under membership inference attacks (MIAs). Prior studies on privacy-preserving of diffusion models fail to balance privacy, utility, and efficiency. To address this gap, we propose a novel framework of privacy-preserving diffusion models, Adaptive Diffusion Freezing (ADF), which can defend against MIAs with better trade-off. By leveraging cross-timestep adaptive freezing training, ADF explicitly control the participation of different data subsets across diffusion timesteps via a mask matrix, which reduces the over-memo...
| Authors | Jialu Guo, Xiao Han, Junjie Wu |
|---|---|
| Primary category | cs.CR |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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