Summary of Rag with Differential Privacy, by Nicolas Grislain
RAG with Differential Privacy
by Nicolas Grislain
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A retrieval-augmented generation (RAG) technique has become the leading method for providing large language models with fresh context, reducing hallucinations and improving response quality in dynamic knowledge environments. However, integrating external documents raises significant privacy concerns, as responses may inadvertently expose confidential data, posing a risk to privacy and ethics. This paper presents a practical solution to address this issue, demonstrating that differentially private token generation is a viable approach for private RAG suitable for general knowledge extraction from personal data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG provides large language models with fresh context, reducing hallucinations and improving response quality in dynamic knowledge environments. But this raises privacy concerns – responses may accidentally expose confidential data. This paper solves the problem by showing that differentially private token generation is a good way to keep RAG private and safe for personal data. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation » Token
