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Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data
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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.
Forward citations
Cited by 8 Pith papers
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FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters
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Distributed Retrieval-Augmented Generation
A distributed RAG framework using topic-aware random walk routing lets edge devices retrieve knowledge from peers with near-centralized accuracy and about half the messages of flooding.
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EnronQA: Towards Personalized RAG over Private Documents
A new 528,304-question benchmark over 103,638 Enron emails across 150 inboxes lets RAG pipelines be evaluated in a setting where LLMs cannot answer from memory.
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Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning
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...
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ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented Generation
ALoFTRAG self-generates Q&A from unlabeled RAG texts, filters them with the same local LLM, and LoRA fine-tunes to lift citation accuracy by 8.3% and answer accuracy by 3.0% on average across 26 languages.
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RAG with Differential Privacy
DP-RAG retrieves top documents with a differentially private threshold and aggregates per-document token probabilities through an exponential mechanism to generate private responses.
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Diverse And Private Synthetic Datasets Generation for RAG evaluation: A multi-agent framework
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.
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Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
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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