Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy

arXiv — cs.CL•Thursday, November 13, 2025 at 5:00:00 AM

The recent study on 'Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy' highlights the challenges of using large language models (LLMs) in sensitive data contexts. As LLMs become more prevalent, the risk of leaking sensitive information through retrieval-augmented generation (RAG) outputs has raised significant privacy concerns. The authors propose an innovative algorithm that optimally allocates privacy budgets, ensuring that only necessary tokens utilize sensitive information while maintaining overall accuracy. Their empirical evaluations indicate that this method outperforms non-RAG baselines under a reasonable privacy budget of approximately 10 across various models and datasets. This research is pivotal as it addresses the pressing need for privacy safeguards in AI, particularly in applications dealing with sensitive data.

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