Assess students' understanding of the trade-offs among privacy, statistical utility, and fairness when designing data‑sharing protocols for public‑health surveillance; includes threat models and regulatory constraints, technical approaches (de‑identification, aggregation, differential privacy, secure multiparty computation, access controls), metrics for utility and subgroup fairness, methods to quantify privacy risk and utility loss, and scenario‑based analysis requiring protocol selection and mitigation strategies to reduce harms to vulnerable populations.