AdaDPSyn introduces a breakthrough technique that generates private synthetic examples for Large Language Models while preserving data privacy.

• Creates differentially private synthetic examples for in-context learning
• Adaptively adjusts noise levels to optimize the privacy-utility tradeoff
• Prevents private information leakage while maintaining model performance
• Enhances security in AI applications across sensitive domains like healthcare and education

This research addresses critical security concerns in AI deployment by enabling organizations to leverage LLMs' capabilities without compromising sensitive information, creating a path toward responsible AI adoption in regulated industries.

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning