Machine unlearning survey

Yiwen Jiang; Shenglong Liu; Tao Zhao; Wei Li; Xianzhou Gao

doi:10.1117/12.2660330

16 December 2022 Machine unlearning survey

Yiwen Jiang, Shenglong Liu, Tao Zhao, Wei Li, Xianzhou Gao

Proceedings Volume 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022); 125006J (2022) https://doi.org/10.1117/12.2660330
Event: 5th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 2022, Chongqing, China

Abstract

Many online platforms have widely deployed machine learning models as a service. Many of these applications require users to upload their data for model training, but it also induces privacy risks. Once the user wants to leave the application, how to make the application unlearn the uploaded data, which is called machine unlearning, is worthy of study. In this article, we provide a survey of machine unlearning with an approximate and exact guarantee. We summarize the existing machine unlearning approaches and discuss their merits and drawbacks in this field.

Citation Download Citation

Yiwen Jiang, Shenglong Liu, Tao Zhao, Wei Li, and Xianzhou Gao "Machine unlearning survey", Proc. SPIE 12500, Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022), 125006J (16 December 2022); https://doi.org/10.1117/12.2660330

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