  Machine Unlearning (MU) has gained considerable attention recently for its
potential to achieve Safe AI by removing the influence of specific data from
trained machine learning models. This process, known as knowledge removal,
addresses AI governance concerns of training data such as quality, sensitivity,
copyright restrictions, and obsolescence. This capability is also crucial for
ensuring compliance with privacy regulations such as the Right To Be Forgotten.
Furthermore, effective knowledge removal mitigates the risk of harmful
outcomes, safeguarding against biases, misinformation, and unauthorized data
exploitation, thereby enhancing the safe and responsible use of AI systems.
Efforts have been made to design efficient unlearning approaches, with MU
services being examined for integration with existing machine learning as a
service, allowing users to submit requests to remove specific data from the
training corpus. However, recent research highlights vulnerabilities in machine
unlearning systems, such as information leakage and malicious unlearning
requests, that can lead to significant security and privacy concerns. Moreover,
extensive research indicates that unlearning methods and prevalent attacks
fulfill diverse roles within MU systems. For instance, unlearning can act as a
mechanism to recover models from backdoor attacks, while backdoor attacks
themselves can serve as an evaluation metric for unlearning effectiveness. This
underscores the intricate relationship and complex interplay among these
mechanisms in maintaining system functionality and safety. This survey aims to
fill the gap between the extensive number of studies on threats, attacks, and
defenses in machine unlearning and the absence of a comprehensive review that
categorizes their taxonomy, methods, and solutions, thus offering valuable
insights for future research directions and practical implementations.
