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Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data

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arxiv 2406.14773 v2 pith:CHWBJQKR submitted 2024-06-20 cs.CR

Shenglai Zeng , Jiankun Zhang , Pengfei He , Jie Ren , Tianqi Zheng , Hanqing Lu , Han Xu , Hui Liu
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Yue Xing Jiliang Tang
This is my paper · ORCID
classification cs.CR
keywords datasyntheticgenerationprivacyinformationretrievaloriginalprivacy-preserving
verification ladder T0 review T1 audit T2 compute T3 formal
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Retrieval-augmented generation (RAG) enhances the outputs of language models by integrating relevant information retrieved from external knowledge sources. However, when the retrieval process involves private data, RAG systems may face severe privacy risks, potentially leading to the leakage of sensitive information. To address this issue, we propose using synthetic data as a privacy-preserving alternative for the retrieval data. We propose SAGE, a novel two-stage synthetic data generation paradigm. In the stage-1, we employ an attribute-based extraction and generation approach to preserve key contextual information from the original data. In the stage-2, we further enhance the privacy properties of the synthetic data through an agent-based iterative refinement process. Extensive experiments demonstrate that using our synthetic data as the retrieval context achieves comparable performance to using the original data while substantially reducing privacy risks. Our work takes the first step towards investigating the possibility of generating high-utility and privacy-preserving synthetic data for RAG, opening up new opportunities for the safe application of RAG systems in various domains.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters

    cs.CL 2026-02 conditional novelty 6.0 of 10

    FedMosaic is a federated RAG system that encodes local documents as mask-gated LoRA adapters, clusters related documents into shared adapters, and selectively merges only relevant, low-conflict adapters at the server.

  2. Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning

    cs.CR 2025-02 reject novelty 6.0 of 10

    RAGC⃝ watermarks RAG knowledge bases by injecting benign chain-of-thought passages that only watermarked queries retrieve, enabling ownership verification via a Wilcoxon test on whether the generated reasoning contain...

  3. Diverse And Private Synthetic Datasets Generation for RAG evaluation: A multi-agent framework

    cs.CL 2025-08 conditional novelty 4.0 of 10

    A multi-agent LLM framework generates synthetic QA datasets for RAG evaluation by combining clustering-based sampling, PII pseudonymization, and QA curation, with reported diversity gains and 0.75-0.90 masking accuracy.

  4. Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey

    cs.CL 2025-02 conditional novelty 4.0 of 10

    A survey organizes current research on trustworthy RAG into six pillars, reliability, privacy, safety, fairness, explainability, and accountability, and maps methods, metrics, and open problems for each.

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