2026
Random Erasing vs. Model Inversion: A Promising Defense or a False Hope?
ICML 2026poster
Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models. While existing defenses primarily concentrate on model-centric approaches, the impact of data on MI robustness remains largely unexplored. In this work, we explore Ran…