Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private

arXiv — cs.LG•Wednesday, November 12, 2025 at 5:00:00 AM

The recent paper 'Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private' presents a crucial advancement in the field of artificial intelligence by addressing privacy concerns associated with retrieval-augmented generation (RAG) systems. Traditional RAG systems enhance large language models (LLMs) by retrieving documents from external sources during inference, but they pose risks of leaking sensitive information. Previous approaches offered differential privacy (DP) guarantees only for single-query scenarios, which are inadequate for practical applications. The authors propose two innovative algorithms, MURAG and MURAG-ADA, designed for multi-query settings. MURAG utilizes an individual privacy filter to limit privacy loss based on document retrieval frequency, while MURAG-ADA enhances utility by releasing query-specific thresholds. Experimental results indicate that these methods can effectively handle hundreds of queries within a practical DP budget, ensuri…

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