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Interview question · Adversarial ML & Model Privacy

Model inversion, model extraction and attribute inference: what is the difference?

Three attacks that all start with queries and end with something the owner did not intend to share. The scored answer separates them by what is stolen and maps each to its defence.

mediumNewPreparation tags: Google DeepMind, Apple, MetaUpdated Nov 2026

Three attacks that all start with queries and end with something the owner did not intend to share. The scored answer separates them by what is stolen and maps each to its defence.

Make your first attempt

What would you say before reading the answer?

  1. 01 State the assumptions your answer depends on.
  2. 02 Explain the mechanism and choose a control.
  3. 03 Name a condition that would change your decision.

The worked answer includes an approach, scoring criteria and follow-up questions. Compare the reasoning with your own.

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What if · the follow-ups in this answer

The answer works through 6 follow-ups like an interviewer would

A team runs a face recognition model behind an API and asks how model inversion, model extraction, attribute inference and membership inference differ.

  1. What does each attack give the attacker?
  2. Why is inversion a concern for this API?
  3. What if an attacker knows some facts about a person?
  4. Do the same defences work for all four?
  5. What output policy would reduce these risks for the API?
  6. How would you protect the face recognition API?

Each rung has a nudge, a model answer, the principle that carries over and the weak answer to avoid. Premium opens every answer and ladder.

UP NEXT ON YOUR JOURNEY
Next in this trackIs anonymising training data enough to stop privacy attacks on a model?Next in this trackExplain DP-SGD, its privacy and utility trade-off, and when differential privacy is the wrong tool.Next in this trackA deployed model may be leaking its training data. How do you respond?
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