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.
CYBERCAT (Center for Cybersecurity Research of Catalonia)（加泰罗尼亚网络安全研究中心）；ComSCIAM-Center for Computational Science and Applied Mathematics（计算科学与应用数学中心）；Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili（罗维拉-维尔吉利大学计算机工程与数学系）
Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation
School of Information Engineering, Zhongnan University of Economics and Law（信息工程学院，中南财经政法大学）；China University of Geosciences, Wuhan（中国地质大学（武汉））；City University of Macau（澳门城市大学）
Auditable Machine Unlearning for Privacy-Compliant Ransomware Detection Using Multi-Shard SISA and Deep Reinforcement Learning
School of Computing, Mathematics and Engineering, Charles Sturt University（查尔斯斯特大学计算、数学与工程学院）；AI and Cyber Futures Centre, Charles Sturt University（查尔斯斯特大学人工智能与网络未来中心）
UnlearnShield: Shielding Forgotten Privacy against Unlearning Inversion
Huazhong University of Science and Technology（华中科技大学）；Institute of Guizhou Aerospace Measuring and Testing Technology（贵州航天测量测试技术研究院）；University of Technology Sydney（悉尼科技大学）；Griffith University（格里菲斯大学）
BlindU: Blind Machine Unlearning without Revealing Erasing Data
School of Computer Science, University of Technology Sydney（计算机科学学院，技术悉尼大学）；School of Cyber Science and Engineering, Southeast University（网络科学与工程学院，东南大学）
University of Virginia（弗吉尼亚大学）；University of Lausanne（洛桑大学）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；Carnegie Mellon University（卡内基梅隆大学）；Purdue University（普渡大学）
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
University of Notre Dame（圣母大学）；University of Pennsylvania（宾夕法尼亚大学）；Dartmouth College（达特茅斯学院）；Georgia Institute of Technology（佐治亚理工学院）；George Mason University（乔治梅森大学）
MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Alibaba-NTU Global e-Sustainability CorpLab (ANGEL)（阿里云-南洋理工大学全球可持续发展科技实验室）；Tongyi Lab, Alibaba Group（通义实验室，阿里巴巴集团）
Data-Free Privacy-Preserving for LLMs via Model Inversion and Selective Unlearning
School of Software Technology, Zhejiang University（浙江大学软件技术学院）；Zhejiang University（浙江大学）；Hefei University of Technology（合肥工业大学）；Institute of Fundamental and Transdisciplinary Research, Zhejiang University（浙江大学基础与交叉学科研究院）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江区）区块链与数据安全研究院）；The State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Zhejiang Key Laboratory of Big Data Intelligent Computing（浙江省大数据智能计算重点实验室）
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
University of Southern Queensland（南昆士兰大学）；Wuhan University of Technology（武汉理工大学）；Lingnan University（岭南大学）；University of Electronic Science and Technology of China（电子科技大学）；Hong Kong Polytechnic University（香港理工大学）
Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data
Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science（齐鲁医院山东大学神经外科和脑科学与脑启发科学研究院）；Department of Neurosurgery, The Affiliated Hospital of Qingdao University（青岛大学附属医院神经外科）；Department of Neurosurgery, Peking Union Medical College Hospital（北京地坛医院神经外科）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；College of Artificial Intelligence, Dalian Maritime University（大连海事大学人工智能学院）；University of Colorado Boulder（科罗拉多大学波尔德分校）；Zhengzhou University（郑州大学）；The University of Hong Kong（香港大学）；Georgia Institute of Technology（佐治亚理工学院）
Textual Unlearning Gives a False Sense of Unlearning
The State Key Laboratory of Blockchain and Data Security, Zhejiang University, P. R. China（区块链与数据安全国家重点实验室，浙江大学，中华人民共和国）；School of Cyber Science and Technology, Zhejiang University, P. R. China（网络安全与技术学院，浙江大学，中华人民共和国）；ETH Zurich, Switzerland（苏黎世联邦理工学院，瑞士）
A Certified Unlearning Approach without Access to Source Data
Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA（加州大学河滨分校电子与计算机工程系）；Brookhaven National Laboratory, Upton, NY, USA（布鲁克海文国家实验室）
Beyond Visual Evidence: Revealing and Mitigating Relational Privacy Leakage in Document MLLMs
Faculty of Engineering, Shenzhen MSU-BIT University（深圳北理莫斯科大学工程学院）；Faculty of CMC, Shenzhen MSU-BIT University（深圳北理莫斯科大学计算机、数学与力学学院）；Department of CDS, Indian Institute of Science（印度科学学院计算机与数据科学系）
