Retrieval-Augmented Generation (RAG) has emerged as the dominant technique to provide \emph{Large Language Models} (LLM) with fresh and relevant context, mitigating the risk of hallucinations and improving the overall quality of responses in environments with large and fast moving knowledge bases. However, the integration of external documents into the generation process raises significant privacy concerns. Indeed, when added to a prompt, it is not possible to guarantee a response will not inadvertently expose confidential data, leading to potential breaches of privacy and ethical dilemmas. This paper explores a practical solution to this problem suitable to general knowledge extraction from personal data. It shows \emph{differentially private token generation} is a viable approach to private RAG.

翻译：检索增强生成（RAG）已成为为大型语言模型提供新鲜且相关上下文的主流技术，它能有效缓解幻觉风险，并在知识库庞大且快速更新的环境中提升整体响应质量。然而，将外部文档整合到生成过程中引发了严重的隐私问题。事实上，当外部文档被添加到提示中时，无法保证响应不会无意间泄露机密数据，这可能导致隐私泄露和伦理困境。本文探讨了一种适用于从个人数据中进行通用知识提取的实用解决方案。研究表明，差分隐私令牌生成是实现隐私保护型RAG的一种可行方法。