  As the right to be forgotten has been legislated worldwide, many studies
attempt to design unlearning mechanisms to protect users' privacy when they
want to leave machine learning service platforms. Specifically, machine
unlearning is to make a trained model to remove the contribution of an erased
subset of the training dataset. This survey aims to systematically classify a
wide range of machine unlearning and discuss their differences, connections and
open problems. We categorize current unlearning methods into four scenarios:
centralized unlearning, distributed and irregular data unlearning, unlearning
verification, and privacy and security issues in unlearning. Since centralized
unlearning is the primary domain, we use two parts to introduce: firstly, we
classify centralized unlearning into exact unlearning and approximate
unlearning; secondly, we offer a detailed introduction to the techniques of
these methods. Besides the centralized unlearning, we notice some studies about
distributed and irregular data unlearning and introduce federated unlearning
and graph unlearning as the two representative directions. After introducing
unlearning methods, we review studies about unlearning verification. Moreover,
we consider the privacy and security issues essential in machine unlearning and
organize the latest related literature. Finally, we discuss the challenges of
various unlearning scenarios and address the potential research directions.
