Privacy-Preserving Experiment Design Questions
Techniques and considerations for designing experiments and data collection strategies that protect privacy. Covers methods such as differential privacy, secure aggregation, federated learning, synthetic data, data minimization, consent management, de-identification, and privacy risk assessment, with emphasis on maintaining data utility and regulatory compliance while enabling robust experimentation.
HardSystem Design
66 practiced
Design an end-to-end protocol that allows several hospitals to jointly train a predictive model using MPC and differential privacy without exposing patient-level data. Include the threat model, whether noise is added locally or centrally, steps for secure aggregation, validation, and practical considerations like computation and communication overhead.
EasyTechnical
75 practiced
List and briefly describe three common differential privacy mechanisms (for example Laplace, Gaussian, Exponential). For each mechanism state typical use cases and the sensitivity assumptions they rely on.
EasyTechnical
76 practiced
Describe a consent management strategy for experiments and analytics. Which consent metadata should be stored (purpose, timestamp, version, scope), how do you support revocation, and how should consent status affect sampling and data retention for A/B tests and model training?
EasyTechnical
56 practiced
Explain the composition property of differential privacy. Provide an intuitive example where you run multiple queries on the same dataset and explain how the overall epsilon accumulates under basic composition, and why advanced composition can provide tighter bounds.
HardTechnical
60 practiced
Formulate a statistical detection test to monitor 'privacy drift' where empirical leakage (measured by membership inference attack success) exceeds the theoretical bound implied by your privacy accounting. Define the null hypothesis, test statistic, sampling plan, and alert thresholds.
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