Privacy-Preserving Techniques for Large Language Model Training: The PUSR Framework and Adaptive Privacy Mechanisms

I present <b>PUSR</b>, a unified framework for evaluating and optimizing privacy-preserving techniques in large language model (LLM) training. Unlike privacy-only, utility-centric, or attack-based approaches, PUSR integrates <b>Privacy, Utility, Scalability, and Resilience</b> into a single interpretable index with strong theoretical guarantees. The framework introduces sensitivity-aware privacy scoring, utility-guided adaptive optimization, and a multi-objective adaptive system that dynamically balances trade-offs while ensuring convergence and Pareto efficiency.Implementation guidelines are provided through a modular pipeline with domain-specific weight configurations, adaptive monitoring, and standardized APIs for privacy assessment and utility tracking. Case study sketches illustrate applications across healthcare, finance, and communication. By unifying evaluation metrics and offering practical deployment guidance, PUSR establishes a principled foundation for reproducible, responsible, and scalable privacy research in LLM training.