Privacy-Preserving RAG via Multi-Agent Semantic Rewriting
The authors propose a multi-agent framework that sanitizes retrieved content in Retrieval-Augmented Generation (RAG) systems through semantic rewriting to prevent privacy leakage from malicious prompts. By employing three specialized agents for privacy extraction, semantic analysis, and reconstruction, the approach removes sensitive identifiers while preserving the core meaning of the text.