AdaDPSyn is a novel algorithm that generates privacy-protected synthetic examples for LLMs while maintaining model effectiveness, solving a critical security challenge in AI deployment.

• Creates differentially private synthetic examples from private datasets
• Adaptively adjusts noise levels to balance privacy and utility
• Enables secure in-context learning without exposing sensitive information
• Particularly valuable for domains with sensitive data like healthcare and education

Security Impact: This research addresses growing concerns about LLMs potentially memorizing and leaking sensitive information from training examples. AdaDPSyn provides organizations a practical way to leverage powerful AI capabilities while maintaining robust privacy guarantees.

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