Privacy in Emerging Technologies and Business Models Questions
Privacy implications of AI/Machine Learning (training data, bias, automated decision-making). Privacy in cloud computing and SaaS models. Privacy in IoT and smart devices. Privacy in big data and analytics. Privacy in blockchain and decentralized systems. Privacy-preserving techniques (differential privacy, federated learning). How privacy requirements evolve with new technologies. Privacy in emerging business models (subscription, data-driven, platform economies).
EasyTechnical
0 practiced
In Python, implement add_laplace_noise(value: float, sensitivity: float, epsilon: float) -> float that returns value plus Laplace noise appropriate for epsilon-differential privacy. Validate parameters and briefly explain how sensitivity and epsilon affect the noise scale. You may assume numpy is available.
MediumTechnical
0 practiced
Compare differential privacy and homomorphic encryption for privacy-preserving machine learning. For each technique, explain the threat model it addresses, typical ML use cases (analytics vs compute-on-encrypted-data), performance and accuracy trade-offs, and practical deployment challenges ML engineers should consider.
MediumSystem Design
0 practiced
Design how to apply differential privacy to aggregate analytics in a multi-tenant SaaS platform where tenants must not learn about other tenants' users. Discuss user-level vs item-level privacy, how to allocate privacy budgets across tenants and dashboards, strategies for caching and aggregation to minimize noise, and impacts to analytics SLAs.
HardTechnical
0 practiced
Explain the basic composition theorem of differential privacy. Show intuitively how privacy loss composes when multiple DP mechanisms are applied sequentially and contrast naive (additive) composition with advanced composition techniques (such as moments accountant or Renyi DP) that yield tighter bounds.
HardTechnical
0 practiced
Propose a data retention and deletion strategy for a large-scale ML ingestion pipeline that supports GDPR/CCPA subject requests and also enables model reproducibility and auditing. Include use of metadata and dataset manifests, pointer-based deletion, snapshotting training recipes, synthetic data alternatives, and trade-offs between retention duration and reproducibility.
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