Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity
arXiv:2606.24623v1 Announce Type: cross Abstract: Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts. To address this, we propose a multi-agent framework that sanitizes retrieved content through semantic rewriting. By employing three specialized agents for privacy extraction, semantic analysis, and reconstruction, our approach collaboratively removes sensitive identifiers while preserving the semantic core. We evaluate the framework on the ChatDoctor and Wiki-PII
The increasing deployment of RAG systems in real-world, sensitive applications necessitates robust privacy solutions as the technology matures.
Ensuring data confidentiality in AI deployments, particularly RAG, is critical for enterprise adoption and compliance, addressing a key bottleneck for advanced AI systems.
This breakthrough offers a method to deploy RAG in sensitive data environments without compromising privacy, potentially expanding its applicability across industries.
- · Enterprises with sensitive data
- · AI-as-a-service providers
- · Privacy tech developers
- · Healthcare and legal sectors
- · AI models without privacy safeguards
- · Organizations non-compliant with data privacy laws
Companies can deploy RAG in privacy-critical applications with greater confidence.
Increased adoption of RAG leads to more sophisticated and personalized AI applications across industries.
New regulatory standards for privacy-preserving AI systems may emerge, impacting the entire AI development lifecycle.
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Read at arXiv cs.AI