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In this paper the use of graph retrieval augmented generation (GRAG) in domain-specific knowledge bases (DSKBs) which contain both public and private data is explored. Our proposed methodology utilizes a modified embedding-based GRAG implementation capable of preserving private information in DSKBs. Our privacy-preserving GRAG methodology is evaluated against a baseline GRAG implementation using a modified version of the MultiHopRAG dataset and based on three metrics: privacy preservation, data quality and computational performance. This research proves that our methodology outperforms the baseline and is capable of preserving private information in DSKBs without compromising on output quality and computational performance.

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Graph Retrieval Augmented Generation for Privacy-Sensitive Information

  • 会议论文

  • 发表日期：

    2026年

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  • Rien Van Campenhout,
  • Benoit De Vrieze,
  • Pieter Jan Houben,
  • Jens de Hoog,
  • Peter Hellinckx

摘要

In this paper the use of graph retrieval augmented generation (GRAG) in domain-specific knowledge bases (DSKBs) which contain both public and private data is explored. Our proposed methodology utilizes a modified embedding-based GRAG implementation capable of preserving private information in DSKBs. Our privacy-preserving GRAG methodology is evaluated against a baseline GRAG implementation using a modified version of the MultiHopRAG dataset and based on three metrics: privacy preservation, data quality and computational performance. This research proves that our methodology outperforms the baseline and is capable of preserving private information in DSKBs without compromising on output quality and computational performance.