[Assess graduate students' ability to design and analyze privacy-preserving statistical queries using Laplace and Gaussian mechanisms. Tasks include computing global/local sensitivity, calibrating noise for given ε (and δ for Gaussian), proving DP guarantees, evaluating utility (error bounds, MSE), reasoning about composition and privacy budgets, and applying mechanisms to counts, means, and histograms with sample calculations and trade-off comparisons.]