Learning to Defend: Auto-Augmentation Search Against Model Inversion Attacks

Summary

This research paper introduces DAAS (Defense via Auto-Augmentation Search) against Model Inversion Attacks, which can recover private training data from model weights or outputs. The authors note that existing defenses offer incomplete protection and that input-level defenses have been limited to simple transformations. DAAS automatically identifies augmentation candidates with strong privacy-utility trade-offs from a large pool, and the authors report superior defense performance across models, datasets and attacks.

Topics

• Training data and privacy

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