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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 4 Pith papers
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FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters
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.
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Differentially Private Synthetic Text Generation for Retrieval-Augmented Generation (RAG)
DP-SynRAG generates reusable differentially private synthetic RAG databases via LLM private prediction to prevent privacy loss accumulation from repeated noise.
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Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents
A data-centric survey finds that only information-flow control covers compositional and cross-session leakage in LLM agents and that no single benchmark tests an agent across all its data surfaces under one policy.
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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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