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Core
Differential Privacy
Differential privacy adds calibrated noise to data, queries, or training so the output is provably insensitive to any single individual's record, bounding what can be learned about any one person. In ML, DP-SGD clips and noises gradients to limit memorization and defend against membership-inference attacks. The cost is a privacy-utility trade-off controlled by a parameter epsilon. Applied-AI interviews probe it because it is the rigorous, mathematically-backed privacy tool, and because models can otherwise memorize and leak training data.
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AI Security, Privacy & GovernanceExplain differential privacy and privacy-preserving ML (DP-SGD, federated learning). When do you use them?→AI Security, Privacy & GovernanceHow do you set and spend an epsilon budget when deploying differential privacy in practice?→AI Security, Privacy & GovernanceWhat are the main privacy-preserving ML techniques, and how do they differ?→AI Security, Privacy & GovernanceYour model regurgitates verbatim training data, including PII. How do you prevent memorization?→AI Security, Privacy & GovernanceWhen is federated learning actually worth it versus centralizing the data?→AI Security, Privacy & GovernanceHow do you handle PII and data governance for an enterprise LLM deployment (SOC 2, GDPR, the EU AI Act)?→
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