Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model
Accuracy
Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model
Accuracy
Machine unlearning algorithms, designed for selective removal of training data from models, have emerged as a promising approach to growing privacy concerns. In this work, we expose a critical yet underexplored vulnerability in the deployment of unlearning systems: the assumption that the data requested for removal is always part of …