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

Summary

CipheRAG is a privacy-preserving retrieval-augmented generation framework for large language models that aims to balance knowledge confidentiality with retrieval efficiency. It combines a searchable inner product functional encryption mechanism, enhanced with asymmetric locality-sensitive hashing, with a decryption-enabled attention mechanism that feeds decrypted knowledge into the generation process. The authors report up to 35x faster generation and 15x faster QKV computation than FHE- and OT-based baselines.

Topics

• Retrieval-augmented generation