Summary of Mind the Privacy Unit! User-level Differential Privacy For Language Model Fine-tuning, by Lynn Chua et al.
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning
by Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Daogao Liu, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 A novel approach to ensuring uniform privacy protection across users in large language models (LLMs) is proposed. The study evaluates two mechanisms, Group Privacy and User-wise DP-SGD, for achieving user-level differential privacy (DP) guarantees during LLM fine-tuning on natural language generation tasks. This addresses the current limitations of treating each example as a privacy unit, which can lead to varying user privacy guarantees. The design choices explored include data selection strategies and parameter tuning for optimizing the privacy-utility tradeoff. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have many uses, but they also raise concerns about personal privacy when used on sensitive information. One way to ensure this privacy is through differential privacy (DP). However, current methods treat each piece of text as a separate unit, which can result in different levels of protection for different people. This study looks at a new approach called user-level DP that ensures the same level of privacy protection for everyone. |
Keywords
» Artificial intelligence » Fine tuning
