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

Abstract

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.

Citation

Sai, S., Mittal, U., Chamola, V., Huang, K., Spinelli, I., Scardapane, S., Tan, Z., & Hussain, A. (2024). Machine Un-learning: An Overview of Techniques, Applications, and Future Directions. Cognitive Computation, 16, 482-506. https://doi.org/10.1007/s12559-023-10219-3

• 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
• DOI: https://doi.org/10.1007/s12559-023-10219-3
• Keywords: Machine unlearning, Privacy, GDPR, Data deletion
• Public URL: http://researchrepository.napier.ac.uk/Output/3373715