{"ID":2875772,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21325","arxiv_id":"2509.21325","title":"PIR-RAG: A System for Private Information Retrieval in Retrieval-Augmented Generation","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.","short_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 c...","url_abs":"https://arxiv.org/abs/2509.21325","url_pdf":"https://arxiv.org/pdf/2509.21325v1","authors":"[\"Baiqiang Wang\",\"Qian Lou\",\"Mengxin Zheng\",\"Dongfang Zhao\"]","published":"2025-09-01T07:28:35Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\",\"cs.CR\"]","methods":"[\"RAG\",\"Large Language Model\"]","has_code":false}