Summary of Efficient Differentially Private Fine-tuning Of Diffusion Models, by Jing Liu et al.
Efficient Differentially Private Fine-Tuning of Diffusion Models
by Jing Liu, Andrew Lowy, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent advancements in Diffusion Models (DMs) have led to the generation of exceptionally high-quality synthetic samples. Building upon previous work that demonstrated the potential of DM-generated synthetic samples for training downstream classifiers while achieving a good privacy-utility tradeoff, this paper investigates efficient fine-tuning methods for large-scale diffusion models using Low-Dimensional Adaptation (LoDA) with Differential Privacy (DP). The proposed Parameter-Efficient Fine-Tuning (PEFT) approach is evaluated on the MNIST and CIFAR-10 datasets, showcasing its ability to generate useful synthetic samples for training downstream classifiers while ensuring the privacy of fine-tuning data. This work has significant implications for the development of robust and privacypreserving AI models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a new way to make large artificial intelligence (AI) models better and more private. These models, called Diffusion Models, can create very realistic fake pictures. Previous research showed that these fake pictures can be used to train other AI models, while keeping the original data safe. However, making these big models work well takes a lot of computer power and memory. To solve this problem, the researchers created a new way to make these models better using less computer resources. They tested their idea on two popular datasets, MNIST and CIFAR-10, and found that it works. This means we can create more AI models that are both good at what they do and protect people’s private information. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Parameter efficient
