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arXiv · 2509.21325

PIR-RAG: A System for Private Information Retrieval in Retrieval-Augmented Generation

Baiqiang Wang·Qian Lou·Mengxin Zheng·Dongfang Zhao
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Abstract

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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Baiqiang Wang, Qian Lou, Mengxin Zheng, Dongfang Zhao. 2025-09-01. PIR-RAG: A System for Private Information Retrieval in Retrieval-Augmented Generation. https://arxiv.org/abs/2509.21325

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