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).
HardTechnical
0 practiced
Explain how differential privacy can interact with fairness goals: can DP worsen fairness (or sometimes help)? Give a concrete example in which adding DP noise could disproportionately hurt a minority group and propose mitigation strategies an AI Engineer might use.
EasyTechnical
0 practiced
Explain the difference between encryption at rest and encryption in transit. In a cloud/SaaS environment for training ML models, indicate which parts of the pipeline should use each and one additional control you would use to protect model artifacts and datasets.
EasyTechnical
0 practiced
Describe model inversion and membership inference attacks in plain terms and give one concise ML-specific mitigation for each. Provide a short example for when an AI Engineer should be particularly worried about each attack.
MediumSystem Design
0 practiced
Design a federated learning architecture for a mobile keyboard suggestion model with 50M active devices. Requirements: protect user privacy, allow low-bandwidth updates, tolerate intermittent connectivity, incorporate secure aggregation, and enable model rollback if poisoned updates are detected. Describe components and data flow.
MediumTechnical
0 practiced
You are evaluating vendors for a labeling pipeline that will handle PII-containing images. Draft a vendor assessment checklist focused on privacy and data protection: technical, legal, operational, and audit controls. Prioritize the top five items and explain why.
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