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Machine Un-learning: An Overview of Techniques, Applications, and Future Directions

Sai, Siva; Mittal, Uday; Chamola, Vinay; Huang, Kaizhu; Spinelli, Indro; Scardapane, Simone; Tan, Zhiyuan; Hussain, Amir


Siva Sai

Uday Mittal

Vinay Chamola

Kaizhu Huang

Indro Spinelli

Simone Scardapane


ML applications proliferate across various sectors. Large internet firms employ ML to train intelligent models using vast datasets, including sensitive user information. However, new regulations like GDPR require data removal by businesses. Deleting data from ML models is more complex than databases. Machine Un-learning (MUL), an emerging field, garners academic interest for selectively erasing learned data from ML models. MUL benefits multiple disciplines, enhancing privacy, security, usability, and accuracy. This article reviews MUL’s significance, providing a taxonomy and summarizing key MUL algorithms. We categorize modern MUL models by criteria, including model independence, data driven, and implementation considerations. We explore MUL applications in smart devices and recommendation systems. We also identify open questions and future research areas. This work advances methods for implementing regulations like GDPR and safeguarding user privacy.

Journal Article Type Article
Acceptance Date Oct 23, 2023
Online Publication Date Nov 4, 2023
Publication Date 2024
Deposit Date Nov 10, 2023
Journal Cognitive Computation
Print ISSN 1866-9956
Electronic ISSN 1866-9964
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 16
Pages 482-506
Keywords Machine unlearning, Privacy, GDPR, Data deletion
Public URL