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.
EasyTechnical
0 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.
HardTechnical
0 practiced
Design a policy and technical system for rotating pseudonymous identifiers used in experiments. Define rotation frequency, retention windows, acceptable re-linking procedures, and safeguards (technical and organizational) to enable necessary longitudinal studies while minimizing privacy risks.
HardTechnical
0 practiced
Optimize noise allocation across multiple related count queries under a global epsilon budget. For example, given counts for age buckets [0-17, 18-25, 26-40, 41-65, 65+] and a single epsilon budget, formulate a convex optimization problem to minimize expected squared error and outline a numerical algorithm to solve it.
MediumTechnical
0 practiced
Implement a privacy accounting function in Python that takes a list of per-step Gaussian mechanism parameters (noise_std, sensitivity, sample_prob) for an iterative algorithm and returns a composite (epsilon, delta) using Renyi Differential Privacy (RDP) conversion. Provide pseudocode or runnable code; you may use numpy but not specialized DP libraries.
MediumSystem Design
0 practiced
Design a privacy-preserving analytics pipeline to compute daily active users (DAU) and 7-day retention without storing raw per-user event logs centrally. Provide high-level architecture components, dataflows, and discuss guarantees and trade-offs for accuracy and privacy.
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