Which of the following methods provides the MOST effective protection against model inversion attacks?

AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model’s tendency to memorize training data and curtails exploitable signal in outputs.

• A (adversarial training) targets perturbation robustness, not primary for inversion.

• B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.

• D (more iterations) typically increases overfitting and leakage risk.

AAISM further notes that privacy-preserving training and output minimization are preferred where feasible; among the listed options, regularization most directly addresses inversion risk.