@inproceedings{zeng-etal-2025-mitigating,
    title = "Mitigating the Privacy Issues in Retrieval-Augmented Generation ({RAG}) via Pure Synthetic Data",
    author = "Zeng, Shenglai  and
      Zhang, Jiankun  and
      He, Pengfei  and
      Ren, Jie  and
      Zheng, Tianqi  and
      Lu, Hanqing  and
      Xu, Han  and
      Liu, Hui  and
      Xing, Yue  and
      Tang, Jiliang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1247/",
    pages = "24538--24569",
    ISBN = "979-8-89176-332-6",
    abstract = "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."
}