Existing work has attempted to alleviate these problems by blurring faces, downsampling videos, or training on synthetic data. On the other hand, analysis on the transferability of privacy-preserving pre-trained models to downstream tasks has been limited.

In this paper, we point out that suboptimal noise-data mapping leads to slow training of diffusion models.

This urges the development of more computation and communication efficient optimization algorithms.