Which of the following is the MOST effective way to prevent a model inversion attack?

Question

Options

• AMonitor model output for anomalies
• BUtilize data pseudonymization
• CImplement differential privacy during model training
• DEnsure data minimization

• A
  13% (7)
• B
  5% (3)
• C
  78% (43)
• D
  4% (2)

Explanation

Model inversion attacks reconstruct sensitive training data by repeatedly querying a model and analyzing its outputs. Differential privacy introduces mathematically calibrated noise during training, providing a formal privacy guarantee that limits how much any single training record influences the model's outputs-making reconstruction statistically infeasible. Option A (monitoring outputs) is a detective control, not preventive. Option B (pseudonymization) protects data at rest/transit but doesn't protect against inversion via the model's learned parameters. Option D (data minimization) reduces exposure but doesn't prevent an attacker from inverting whatever the model did learn.

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