Assess students' understanding of trade-offs between healthcare data-privacy regulations (e.g., HIPAA, GDPR) and machine learning model performance: include technical approaches (de-identification, k‑anonymity, differential privacy, federated learning, SMPC), evaluation of utility-privacy trade-offs and performance metrics, compliance and governance considerations, and practical strategies to optimize models while managing legal, ethical, and risk constraints; expect analysis of brief case scenarios and justification of chosen mitigation tactics.