Differentially Private Latent Diffusion Models

Diffusion models (DMs) are widely used for generating high-quality highdimensional images in a non-differentially private manner. However, due to the notoriously slow training process of DMs, applying differential privacy (DP) to the training routine requires adding large amounts of noise, yielding poor-quality generated images. To address this challenge, recent papers suggest pre-training DMs with public data, then fine-tuning them with private data using DP-SGD for a relatively short period. In this paper, we further improve the current state of DMs with DP by adopting the Latent Diffusion Models (LDMs). LDMs are equipped with powerful pre-trained autoencoders that map the high-dimensional pixels into lower-dimensional latent representations, in which DMs are trained, yielding a more efficient and fast training of DMs. In our algorithm, DP-LDMs, rather than fine-tuning the entire DMs, we fine-tune only the attention modules of LDMs at varying layers with privacy-sensitive data, reducing the number of trainable parameters by roughly 90% and achieving a better accuracy, compared to fine-tuning the entire DMs. The smaller parameter space to fine-tune with DP-SGD helps our algorithm to achieve new state-of-the-art results in several public-private benchmark data pairs. Our approach also allows us to generate more realistic, high-dimensional images (256x256) and those conditioned on text prompts with differential privacy, which have not been attempted before us, to the best of our knowledge. Our approach provides a promising direction for training more powerful, yet training-efficient differentially private DMs, producing high-quality high-dimensional DP images. Creating impactful machine learning solutions for real-world applications often requires access to personal data that may compromise privacy, raising ethical and legal concerns.