Towards Independence Criterion in Machine Unlearning of Features and Labels

arXiv:2403.08124v1 Announce Type: cross
Abstract: This work delves into the complexities of machine unlearning in the face of distributional shifts, particularly focusing on the challenges posed by non-uniform feature and label removal. With the advent of regulations like the GDPR emphasizing data privacy and the right to be forgotten, machine learning models face the daunting task of unlearning sensitive information without compromising their integrity or performance. Our research introduces a novel approach that leverages influence functions and principles of distributional …

arxiv challenges complexities cs.ai cs.cr cs.lg data data privacy feature features gdpr machine non privacy regulations right to be forgotten shifts work