  The explosive growth of machine learning has made it a critical
infrastructure in the era of artificial intelligence. The extensive use of data
poses a significant threat to individual privacy. Various countries have
implemented corresponding laws, such as GDPR, to protect individuals' data
privacy and the right to be forgotten. This has made machine unlearning a
research hotspot in the field of privacy protection in recent years, with the
aim of efficiently removing the contribution and impact of individual data from
trained models. The research in academia on machine unlearning has continuously
enriched its theoretical foundation, and many methods have been proposed,
targeting different data removal requests in various application scenarios.
However, recently researchers have found potential privacy leakages of various
of machine unlearning approaches, making the privacy preservation on machine
unlearning area a critical topic. This paper provides an overview and analysis
of the existing research on machine unlearning, aiming to present the current
vulnerabilities of machine unlearning approaches. We analyze privacy risks in
various aspects, including definitions, implementation methods, and real-world
applications. Compared to existing reviews, we analyze the new challenges posed
by the latest malicious attack techniques on machine unlearning from the
perspective of privacy threats. We hope that this survey can provide an initial
but comprehensive discussion on this new emerging area.
