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.
MediumTechnical
70 practiced
Design an experiment-level privacy policy and consent UI for running A/B tests that include personalization. Specify the consent language, the granular choices users should have, and how consent toggles map to data collection and processing flows.
EasyTechnical
119 practiced
Compare and contrast de-identification, pseudonymization, and anonymization. For each term define it, describe common techniques (masking, hashing, generalization), and explain situations where de-identification may still allow re-identification risk.
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?
MediumSystem Design
58 practiced
Design an A/B test to evaluate a new recommendation model while preserving user privacy. Constraints: 100k daily active users, per-user privacy budget epsilon_total=1 for the experiment, and raw clickstreams cannot be centralized. Describe user assignment, metric collection, aggregated computation, how to respect per-user budget, and trade-offs in metric precision.
EasyTechnical
75 practiced
Explain in your own words what differential privacy guarantees about an algorithm's outputs. Include the formal (epsilon,delta)-differential privacy definition, explain the meaning of neighboring datasets, and provide a simple intuitive example showing how adding noise protects an individual's presence in a dataset.
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