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Core
Differential Privacy
Differential privacy injects calibrated noise into data, queries, or training so the output is provably insensitive to any single individual's record, capping what can be learned about any one person. In ML, DP-SGD clips and noises gradients to curb memorization and defend against membership-inference attacks. The price is a privacy-utility trade-off governed by a parameter epsilon. AI, ML, and GenAI 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 & GovernanceYour differentially private model lost most of its accuracy. How do you buy the utility back?→
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