让AI查询更私密，高效获取文档不暴露用户意图。
PIR-RAG: A System for Private Information Retrieval in Retrieval-Augmented Generation
- 用语义聚类缩小搜索范围，结合快速隐私检索协议。
- 端到端延迟更低，支持大规模AI系统实时响应。
- 适合注重隐私的智能问答、客服等应用场景。
检索增强生成（RAG）已成为现代AI系统的核心组件，但会将用户查询暴露给服务提供方，带来重大隐私风险。为此，我们提出PIR-RAG，一个面向隐私保护RAG的实用系统。PIR-RAG采用新型架构，利用粗粒度语义聚类来缩减搜索空间，并结合一种快速的基于格的私有信息检索（PIR）协议。该设计可高效检索整个文档簇，特别优化了需要完整文档内容的端到端RAG流程。我们在强基线架构（包括基于图的PIR和Tiptoe风格私有评分）上进行全面评估，结果表明PIR-RAG具备良好的可扩展性，在“RAG就绪延迟”——即LLM安全获取内容所需的真正端到端时间——方面表现更优。本工作确立了PIR-RAG作为大规模AI系统中隐私保护的可行且高效方案。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has become a foundational component of modern AI systems, yet it introduces significant privacy risks by exposing user queries to service providers. To address this, we introduce PIR-RAG, a practical system for privacy-preserving RAG. PIR-RAG employs a novel architecture that uses coarse-grained semantic clustering to prune the search space, combined with a fast, lattice-based Private Information Retrieval (PIR) protocol. This design allows for the efficient retrieval of entire document clusters, uniquely optimizing for the end-to-end RAG workflow where full document content is required. Our comprehensive evaluation against strong baseline architectures, including graph-based PIR and Tiptoe-style private scoring, demonstrates PIR-RAG's scalability and its superior performance in terms of "RAG-Ready Latency"-the true end-to-end time required to securely fetch content for an LLM. Our work establishes PIR-RAG as a viable and highly efficient solution for privacy in large-scale AI systems.
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