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

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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.

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Funding

This work has been supported by Engineering and Physical Sciences Research Council (EPSRC) Grants Ref. EP/M026981/1, EP/T021063/1, EP/T024917/1 and ENU Development Trust Ref. LH Oct19’.

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  1. Birla Institute of Technology and Science - Pilani Campus: Birla Institute of Technology & Science Pilani, Pilani, Rajasthan, India

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

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  1. Siva Sai
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  2. Uday Mittal
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  3. Vinay Chamola
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Correspondence to Vinay Chamola.

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Sai, S., Mittal, U., Chamola, V. et al. Machine Un-learning: An Overview of Techniques, Applications, and Future Directions. Cogn Comput 16, 482–506 (2024). https://doi.org/10.1007/s12559-023-10219-3

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