This research introduces a privacy-aware framework for Retrieval-Augmented Generation (RAG) systems that protects sensitive information in knowledge bases while maintaining performance.

• Addresses critical security vulnerabilities in conventional RAG implementations
• Proposes an advanced encryption methodology to safeguard proprietary data
• Enables secure knowledge retrieval while preserving the utility of LLM outputs
• Balances performance and privacy for real-world enterprise applications

This breakthrough matters because it enables organizations to deploy RAG systems with confidence that their sensitive information remains protected from unauthorized access or extraction attempts.

Privacy-Aware RAG: Secure and Isolated Knowledge Retrieval