A Review on Machine Unlearning Algorithms and Privacy Protection

Abstract

In recent years, machine learning algorithms are increasingly dependent on large volumes of data for their training, including personal data, while at the same time the law has strengthened the right of individuals to have such data deleted, thus creating an inherent tension. Regulations such as the General Data Protection Regulation (GDPR) oblige an organization to erase personal data on request, but deleting a record from a database is not enough. A trained model retains the influence of that record in its parameters and may still expose it, for example, through membership inference. Machine unlearning has emerged in order to remove this influence from the model itself, and it has rapidly developed into an active research area. However, existing surveys have not provided a unified, verifiability-centered account of what is required to demonstrate that unlearning has actually occurred. This review provides a unified treatment of machine unlearning, beginning with the taxonomy of exact and approximate algorithms and the trade-off between efficacy, fidelity, and efficiency that governs them. It then examines the role of unlearning in privacy protection and its dual role in security, where it serves as a defense against poisoning and backdoors but also becomes an attack surface. Particular attention is given to evaluation, because the empirical tests of the literature can measure a removal but cannot prove it. On this basis, the review examines verifiable, federated, and decentralized unlearning, including the Proof of Unlearning and zero-knowledge constructions. Taken together, the review’s findings indicate that most methods assert rather than prove removal, while verifiable unlearning in federated and decentralized environments remains a central open problem.