Differentially Private Fine-Tuning of Diffusion Models

TL;DR

This paper introduces a parameter-efficient fine-tuning method for differentially private diffusion models, significantly improving privacy-utility trade-offs and achieving state-of-the-art results in private image synthesis.

Contribution

We propose a novel, parameter-efficient fine-tuning approach tailored for private diffusion models, enhancing performance while reducing the number of trainable parameters.

Findings

Achieved over 35% improvement in DP synthesis on CelebA-64.

Reduced trainable parameters to 0.47 million, outperforming previous methods.

Demonstrated state-of-the-art results with small privacy budgets.

Abstract

The integration of Differential Privacy (DP) with diffusion models (DMs) presents a promising yet challenging frontier, particularly due to the substantial memorization capabilities of DMs that pose significant privacy risks. Differential privacy offers a rigorous framework for safeguarding individual data points during model training, with Differential Privacy Stochastic Gradient Descent (DP-SGD) being a prominent implementation. Diffusion method decomposes image generation into iterative steps, theoretically aligning well with DP's incremental noise addition. Despite the natural fit, the unique architecture of DMs necessitates tailored approaches to effectively balance privacy-utility trade-off. Recent developments in this field have highlighted the potential for generating high-quality synthetic data by pre-training on public data (i.e., ImageNet) and fine-tuning on private data, however, there is a pronounced gap in research on optimizing the trade-offs involved in DP settings, particularly concerning parameter efficiency and model scalability. Our work addresses this by proposing a parameter-efficient fine-tuning strategy optimized for private diffusion models, which minimizes the number of trainable parameters to enhance the privacy-utility trade-off. We empirically demonstrate that our method achieves state-of-the-art performance in DP synthesis, significantly surpassing previous benchmarks on widely studied datasets (e.g., with only 0.47M trainable parameters, achieving a more than 35% improvement over the previous state-of-the-art with a small privacy budget on the CelebA-64 dataset). Anonymous codes available at https://anonymous.4open.science/r/DP-LORA-F02F.