2026
Learnability and Privacy Vulnerability are Entangled in a Few Critical Weights
ICLR 2026poster
Prior approaches for membership privacy preservation usually update or retrain all weights in neural networks, which is costly and can lead to unnecessary utility loss or even more serious misalignment in predictions between training data and non-training data. In this paper, we empirically show tha…