arXiv Artificial Intelligence

Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs

1 hour ago 14.08.2026 07:00 👁 1 views
Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs

Quick summary

arXiv:2608.12675v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely used to improve the performance of Large Language Models (LLMs) in answering user queries. Existing privacy research on RAG has focused on preventing unauthorized users from accessing sensitive data. However, another important problem that is often overlooked in RAG privacy research is that external generators have access to the query and the retrieved documents, which may contain confidential information that could potentially be misused or accessed for unintended purposes. In this paper, we introdu

Key takeaways

  • arXiv:2608.12675v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely used to improve the performance of Large Language Models (LLMs) in answering user queries.
  • Existing privacy research on RAG has focused on preventing unauthorized users from accessing sensitive data.
  • However, another important problem that is often overlooked in RAG privacy research is that external generators have access to the query and the retrieved documents, which may contain confidential information that could potentially be misused or accessed for unintended purposes.

Why it matters

“Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗