Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects

• URL: http://arxiv.org/abs/2403.08254v1
• Date: Wed, 13 Mar 2024 05:11:24 GMT
• Title: Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
• Authors: Na Li, Chunyi Zhou, Yansong Gao, Hui Chen, Anmin Fu, Zhi Zhang, and Yu Shui
• Abstract summary: Data users have been endowed with the right to be forgotten of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data. Machine unlearning emerges to address this, which has garnered ever-increasing attention from both industry and academia.
• Score: 17.502158848870426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy. Data users have been endowed with the right to be forgotten of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data and its subsequent impact on ML models upon user requests. Machine unlearning emerges to address this, which has garnered ever-increasing attention from both industry and academia. While the area has developed rapidly, there is a lack of comprehensive surveys to capture the latest advancements. Recognizing this shortage, we conduct an extensive exploration to map the landscape of machine unlearning including the (fine-grained) taxonomy of unlearning algorithms under centralized and distributed settings, debate on approximate unlearning, verification and evaluation metrics, challenges and solutions for unlearning under different applications, as well as attacks targeting machine unlearning. The survey concludes by outlining potential directions for future research, hoping to serve as a guide for interested scholars.

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