Efficient Vector-Multiplicative Privacy-Preserving Retrieval-Augmented Generation for Large Language Models

This paper introduces CipheRAG, a system that helps large language models (LLMs) safely use external knowledge sources while protecting sensitive data. The system balances two competing needs: keeping data private while still retrieving information quickly, which existing approaches struggle to do because cryptography-based methods are slow while faster methods leak more information.