mlsanityData to 14 Oct 2026

Trans-RAG: Query-Centric Vector Transformation for Secure Cross-Organizational Retrieval

arXiv 2604.09541 ↗First posted 10 Apr 2026cs.CR cs.IRcited by 0

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

Abstract (arXiv)

Retrieval Augmented Generation (RAG) systems deployed across organizational boundaries face fundamental tensions between security, accuracy, and efficiency. Current encryption methods expose plaintext during decryption, while federated architectures prevent resource integration and incur substantial overhead. We introduce Trans-RAG, implementing a novel vector space language paradigm where each organization's knowledge exists in a mathematically isolated semantic space. At the core lies vector2Trans, a multi-stage transformation technique that enables queries to dynamically "speak" each organization's vector space "language" through query-centric transformations, eliminating decryption overhead while maintaining native retrieval efficiency. Security evaluations demonstrate near-orthogonal vector spaces with 89.90deg angular separation and 99.81% isolation rates. Experiments across 8 retrievers, 3 datasets, and 3 LLMs show minimal accuracy degradation (3.5% decrease in nDCG@10) and significant efficiency improvements over homomorphic encryption.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 16 of its 40 references that are in the map. First posted on arXiv 10 Apr 2026.

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16 of 40 references.

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Same direction, newest first

  1. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)23 Feb

Other references · 15

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  4. 2017Billion-scale similarity search with GPUscited by 2,497
  5. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  6. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  7. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 981
  8. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 723
  9. 2020Fact or Fiction: Verifying Scientific Claimscited by 554
  10. 2023Text Embeddings Reveal (Almost) As Much As Textcited by 192
  11. 2024Jasper and Stella: distillation of SOTA embedding modelscited by 112
  12. 2019A Mutual Information Maximization Perspective of Language Representation Learningcited by 85
3 more
  1. 2024Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queriescited by 25
  2. 2023Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approachescited by 8
  3. 2021Prism: Private Verifiable Set Computation over Multi-Owner Outsourced Databasescited by 3

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  4. 2026Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks
  5. 2026Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions
  6. 2025Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering
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