differential privacy
AI security interview questions tagged differential privacy, across every topic.
4 questions · 1 unlocked for you
Concepts behind "differential privacy"
Explanations of the mechanisms these questions rely on.
Foundational
Membership Inference AttacksA membership inference attack tries to determine whether a specific record was part of a model's training data, using the model's outputs. Models behave measurably differently on data they were trained on, especially when they overfit, and an attacker can exploit that gap. For sensitive datasets, membership itself is private information. Differential privacy is the principled defence; regularisation, limiting outputs and data minimisation reduce risk. Adversarial ML & Privacy
Foundational
Training-Data Memorisation and ExtractionLanguage models memorise part of their training data verbatim, and an attacker can sometimes make them emit it: personal data, secrets, copyrighted text. Memorisation grows with duplication, model size and prompt context, happens before overfitting, and survives alignment, which only hides it. Defences stack: deduplicate, scrub, train with differential privacy where it fits, filter outputs knowing approximate copies slip through, and measure with canaries and extraction tests. Fine-tuning on customer data is the highest-risk case. Adversarial ML & Privacy