This research examines the critical balance between privacy protection, model performance, and computational efficiency when fine-tuning large language models.

• Evaluates differentially private (DP) training methods that reduce privacy risks but significantly increase computational costs
• Compares various fine-tuning approaches to identify optimal trade-offs between privacy, utility, and efficiency
• Provides frameworks for measuring privacy risk exposure during the fine-tuning process
• Offers practical guidance for secure LLM adaptation in resource-constrained environments

For security teams, this research delivers actionable insights on protecting sensitive training data while maintaining model performance, essential for deploying LLMs in privacy-sensitive domains.

Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models