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arXiv · 2604.26525

PRAG: End-to-End Privacy-Preserving Retrieval-Augmented Generation

Zhijun Li·Minghui Xu·Huayi Qi·Wenxuan Yu·Tingchuang Zhang·Qiao Zhang·GuangYong Shang·Zhen Ma·Xiuzhen Cheng
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Abstract

Retrieval-Augmented Generation (RAG) is essential for enhancing Large Language Models (LLMs) with external knowledge, but its reliance on cloud environments exposes sensitive data to privacy risks. Existing privacy-preserving solutions often sacrifice retrieval quality due to noise injection or only provide partial encryption. We propose PRAG, an end-to-end privacy-preserving RAG system that achieves end-to-end confidentiality for both documents and queries without sacrificing the scalability of cloud-hosted RAG. PRAG features a dual-mode architecture: a non-interactive PRAG-I utilizes homomorphic-friendly approximations for low-latency retrieval, while an interactive PRAG-II leverages client assistance to match the accuracy of non-private RAG. To ensure robust semantic ordering, we introduce Operation-Error Estimation (OEE), a mechanism that stabilizes ranking against homomorphic noise. Experiments on large-scale datasets demonstrate that PRAG achieves competitive recall (72.45%-74.45%), practical retrieval latency, and strong resilience against graph reconstruction attacks while maintaining end-to-end confidentiality. This work confirms the feasibility of secure, high-performance RAG at scale.

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Zhijun Li, Minghui Xu, Huayi Qi, Wenxuan Yu, Tingchuang Zhang, Qiao Zhang, GuangYong Shang, Zhen Ma, Xiuzhen Cheng. 2026-04-29. PRAG: End-to-End Privacy-Preserving Retrieval-Augmented Generation. https://arxiv.org/abs/2604.26525

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