Global Privacy Regulations and Data Protection Frameworks Questions
The landscape of privacy and data protection law and how core frameworks fit together: controllers vs processors, personal vs sensitive data, lawful processing, and cross-framework concepts. Covers foundational privacy terminology and how to reason about which regimes apply to a given data flow. Serves as the orientation layer beneath the regulation-specific topics.
Explain practical steps to ensure ML models conform to data protection laws like GDPR/CCPA: (1) define key obligations simply (data minimization, consent, right to erasure), (2) step-by-step engineering controls (data inventories, pseudonymization, retention policies, request workflows), (3) document examples of how to handle deletion and model retraining, (4) explain why legal compliance affects architecture and risk management.
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