Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and
  Prospects
Na Li Chunyi Zhou Yansong Gao Hui Chen Anmin Fu Zhi Zhang Yu Shui
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.