This research introduces a novel method for transferring domain-specific knowledge to LLMs while protecting sensitive data.

• Addresses the privacy-utility tradeoff that existing methods like RAG struggle with
• Proposes a model-based knowledge transfer approach that maintains privacy constraints
• Enables organizations to leverage LLMs with domain-specific knowledge without exposing sensitive information
• Particularly valuable for security-critical applications where data privacy is paramount

This innovation helps organizations deploy LLM solutions in regulated industries by ensuring sensitive data remains protected while still enabling access to specialized knowledge and capabilities.

Model-Based Privacy-Preserving Knowledge Transfer for Large Language Models