PIR-RAG: A System for Private Information Retrieval in Retrieval-Augmented Generation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909806477967360 |
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| author | Wang, Baiqiang Lou, Qian Zheng, Mengxin Zhao, Dongfang |
| author_facet | Wang, Baiqiang Lou, Qian Zheng, Mengxin Zhao, Dongfang |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_21325 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PIR-RAG: A System for Private Information Retrieval in Retrieval-Augmented Generation Wang, Baiqiang Lou, Qian Zheng, Mengxin Zhao, Dongfang Information Retrieval Artificial Intelligence Cryptography and Security 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. |
| title | PIR-RAG: A System for Private Information Retrieval in Retrieval-Augmented Generation |
| topic | Information Retrieval Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2509.21325 |