DUCK: Distance-based Unlearning via Centroid Kinematics
Marco Cotogni Jacopo Bonato Luigi Sabetta Francesco Pelosin Alessandro Nicolosi
Machine Unlearning is rising as a new field, driven by the pressing necessity
of ensuring privacy in modern artificial intelligence models. This technique
primarily aims to eradicate any residual influence of a specific subset of data
from the knowledge acquired by a neural model during its training. This work
introduces a novel unlearning algorithm, denoted as Distance-based Unlearning
via Centroid Kinematics (DUCK), which employs metric learning to guide the
removal of samples matching the nearest incorrect centroid in the embedding
space. Evaluation of the algorithm's performance is conducted across various
benchmark datasets in two distinct scenarios, class removal, and homogeneous
sampling removal, obtaining state-of-the-art performance. We introduce a novel
metric, called Adaptive Unlearning Score (AUS), encompassing not only the
efficacy of the unlearning process in forgetting target data but also
quantifying the performance loss relative to the original model. Moreover, we
propose a novel membership inference attack to assess the algorithm's capacity
to erase previously acquired knowledge, designed to be adaptable to future
methodologies.