mlsanityData to 14 Oct 2026

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

arXiv 2509.21325 ↗First posted 1 Sep 2025cs.IR cs.AI cs.CRcited by 4

On the map: Retrieval-augmented generation → Privacy and security of RAG

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 2 of its 11 references that are in the map. First posted on arXiv 1 Sep 2025.

Builds on

2 of 11 references.

References · 2

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 861

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Spruce: Scalable Private Outsourced Retrieval Using Compact Embeddingscited by 2
  2. 2026Masking or Mitigating? Deconstructing the Impact of Query Rewriting on Retriever Biases in RAGcited by 0
  3. 2026Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agentscited by 0
  4. 2026Quantization Enables Private Dense Retrieval against Malicious Service Providerscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026PILLAR: Private Inverted-Index Lexical Lookup for Augmented Retrieval
  2. 2026P2RAG: Efficient Privacy-Preserving RAG Service Supporting Arbitrary Top-k Retrieval
  3. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems
  4. 2026PRAG: End-to-End Privacy-Preserving Retrieval-Augmented Generation
  5. 2025Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private
  6. 2024RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service
  7. 2024Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data
  8. 2024Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy