This research introduces CTCL, a novel approach for synthesizing privacy-protected text data that doesn't require full LLM finetuning.

• Creates synthetic data with differential privacy guarantees without costly computation
• Leverages constrained text clustering to identify high-quality private examples
• Achieves better utility-privacy trade-offs than existing methods
• Enables organizations with limited resources to implement privacy-preserving AI solutions

For security professionals, this research offers practical ways to generate synthetic training data while maintaining strict privacy standards—addressing a critical challenge in regulated industries like healthcare and finance where data privacy is paramount.

Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs