This research introduces a privacy-preserving RAG framework with differential privacy guarantees to prevent leakage of sensitive information when using LLMs with external data sources.

• Implements differential privacy mechanisms specifically designed for RAG systems
• Addresses critical privacy concerns in domains with highly sensitive data (healthcare, finance)
• Enables organizations to safely leverage LLMs with proprietary or confidential information
• Balances privacy protection with maintaining useful RAG outputs

This advancement is crucial for security teams implementing LLMs in regulated environments, as it provides mathematical privacy guarantees rather than heuristic approaches, significantly reducing compliance and data breach risks.

Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy