Summary of Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions, by Hao Du et al.
Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions
by Hao Du, Shang Liu, Lele Zheng, Yang Cao, Atsuyoshi Nakamura, Lei Chen
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Fine-tuning Large Language Models (LLMs) enables state-of-the-art performance across various domains. However, this process involves sensitive datasets, introducing privacy risks that exploit the unique characteristics of fine-tuning. This paper provides a comprehensive survey of privacy challenges associated with fine-tuning LLMs, highlighting vulnerabilities to membership inference, data extraction, and backdoor attacks. Defense mechanisms like differential privacy, federated learning, and knowledge unlearning are reviewed, discussing their effectiveness in addressing privacy risks while maintaining model utility. The paper identifies key gaps in existing research, proposing directions to advance the development of privacy-preserving methods for fine-tuning LLMs, promoting responsible use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a problem with using special language models (LLMs) for specific tasks. When we adjust these models to work well for a particular job, we need to be careful not to share private information that could be used against us. The researchers identify some big problems in keeping this information safe and review ways to solve these issues while still making the models work well. They also point out areas where more research is needed to make sure these language models are used safely. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Inference
