Modern privacy regulations grant citizens the right to be forgotten by products, services and companies. In case of machine learning (ML) applications, this necessitates deletion of data not only from storage archives but also from ML models. Due to an increasing need for regulatory compliance required for ML applications, machine unlearning is becoming an emerging research problem. The right to be forgotten requests come in the form of removal of a certain set or class of data from the already trained ML model. Practical considerations preclude retraining of the model from scratch after discarding the deleted data. The few existing studies use either the whole training data, or a subset of training data, or some metadata stored during training to update the model weights for unlearning. However, in many cases, no data related to the training process or training samples may be accessible for the unlearning purpose. We therefore ask the question: is it possible to achieve unlearning with zero training samples? In this paper, we introduce the novel problem of zero-shot machine unlearning that caters for the extreme but practical scenario where zero original data samples are available for use. We then propose two novel solutions for zero-shot machine unlearning based on (a) error minimizing-maximizing noise and (b) gated knowledge transfer. These methods remove the information of the forget data from the model while maintaining the model efficacy on the retain data. The zero-shot approach offers good protection against the model inversion attacks and membership inference attacks. We introduce a new evaluation metric, Anamnesis Index (AIN) to effectively measure the quality of the unlearning method. The experiments show promising results for unlearning in deep learning models on benchmark vision data-sets. The source code is available here: https://github.com/ayu987/zero-shot-unlearning
Machine unlearning through fine-grained model parameters perturbation
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与电子工程学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Shenzhen Research Institute, Hunan University（湖南大学深圳研究院）；Faculty of Computing, Engineering & Media, De Montfort University（德蒙福特大学计算、工程与媒体学院）
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）
University of Trento（特伦托大学）；University of Bergamo（贝加莫大学）；Inria Grenoble（格勒诺布尔研究所）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
On the importance of multiple training seeds for evaluating machine unlearning
Department of Computer Science（计算机科学系）；School of Mathematics（数学学院）；School of Science（科学学院）；Statistics and Physics（统计学与物理学）；Loughborough University（洛桑大学）；Newcastle University（新castle大学）
Module-Aware Parameter-Efficient Machine Unlearning on Transformers
The state Key Laboratory of Blockchain and Data Security, Zhejiang University（区块链与数据安全省重点实验室，浙江大学）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）
Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs
Dept. of Computer Science & Munich Data Science Institute, Technical University of Munich（计算机科学系及慕尼黑数据科学研究所，技术大学慕尼黑）；Mila, Université de Montréal（蒙特利尔大学Mila）
Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors
State Key Laboratory for Novel Software Technology Nanjing University（南京大学计算机软件新技术国家重点实验室）；Centre for Cyber Resilience and Trust Deakin University（迪肯大学网络韧性与信任中心）
Efficient Machine Unlearning via Influence Approximation
School of Computer Science and Technology, University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；School of Artificial Intelligence and Data Science, University of Science and Technology of China（人工智能与数据科学学院，中国科学技术大学）；Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China（安徽省大数据分析与应用重点实验室，计算机科学与技术学院，中国科学技术大学）
Agentic Unlearning: When LLM Agent Meets Machine Unlearning
Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine（山东中医药大学中医人工智能中心）；School of Software, Shandong University（山东大学软件学院）；School of Medical Information Engineering, Shandong University of Traditional Chinese Medicine（山东中医药大学医学信息工程学院）；Shandong Huazhi Talent Technology Co., Ltd.（山东华智人才科技有限公司）
Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
School of Information, Renmin University of China（中国人民大学信息学院）；School of Computer Science and Big Data, Minjiang University（闽江学院计算机与大数据学院）；RMIT University（皇家墨尔本理工大学）
Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
Department of Electrical and Computer Engineering（电气与计算机工程系）；Georgia Institute of Technology（佐治亚理工学院）；Department of Mathematics（数学系）；Massachusetts Institute of Technology（麻省理工学院）
