Verifying whether the machine unlearning process has been properly executed is critical but remains underexplored. Some existing approaches propose unlearning verification methods based on backdooring techniques. However, these methods typically require participation in the model's initial training phase to backdoor the model for later verification, which is inefficient and impractical. In this paper, we propose an efficient verification of erasure method (EVE) for verifying machine unlearning without requiring involvement in the model's initial training process. The core idea is to perturb the unlearning data to ensure the model prediction of the specified samples will change before and after unlearning with perturbed data. The unlearning users can leverage the observation of the changes as a verification signal. Specifically, the perturbations are designed with two key objectives: ensuring the unlearning effect and altering the unlearned model's prediction of target samples. We formalize the perturbation generation as an adversarial optimization problem, solving it by aligning the unlearning gradient with the gradient of boundary change for target samples. We conducted extensive experiments, and the results show that EVE can verify machine unlearning without involving the model's initial training process, unlike backdoor-based methods. Moreover, EVE significantly outperforms state-of-the-art unlearning verification methods, offering significant speedup in efficiency while enhancing verification accuracy. The source code of EVE is released at \uline{https://anonymous.4open.science/r/EVE-C143}, providing a novel tool for verification of machine unlearning.
From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models
Shanghai University of Finance and Economics（上海财经大学）；Beihang University（北京航空航天大学）；Hefei University of Technology（合肥工业大学）；University of South Florida（南佛罗里达大学）；University of Maryland（马里兰大学）
Towards Federated Domain Unlearning: Verification Methodologies and Challenges
University of Macau（澳门大学）；University Of Electronic Science And Technology（电子科技大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究院）
Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs
The Hong Kong Polytechnic University（香港理工大学）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Santa Cruz（加州大学圣克ruz分校）；Huawei Technologies（华为技术有限公司）；Research Centre for Privacy and Security Technologies in Future Smart Systems, PolyU（未来智能系统中的隐私与安全技术研究中心，PolyU）
School of Computing and Information Systems（计算与信息系统学院）；University of Melbourne（墨尔本大学）；Department of Computer Science（计算机科学系）；Amherst College（阿默斯特学院）
The right to erasure requires removal of a user's information from data held by organizations, with rigorous interpretations extending to downstream products such as learned models. Retraining from scratch with the particular user's data omitted fully removes its influence on the resulting model, but comes with a high computational cost. Machine "unlearning" mitigates the cost incurred by full retraining: instead, models are updated incrementally, possibly only requiring retraining when approximation errors accumulate. Rapid progress has been made towards privacy guarantees on the indistinguis…
Bias Redistribution in Visual Machine Unlearning: Does Forgetting One Group Harm Another?
NewraLab；Beihang University（北京航空航天大学）；GoerTek Alpha Lab（歌尔Alpha实验室）；Skyline University Nigeria（尼日利亚天际线大学）；UESTC（电子科技大学）；Imperial College London（帝国理工学院）
HKUST（香港科技大学）；University of Cambridge（剑桥大学）；Central South University（中南大学）；Shanghai Jiaotong University（上海交通大学）；SenseTime Research（商汤科技研究院）；Tsinghua University（清华大学）
$\textbf{AGT$^{AO}$}$: Robust and Stabilized LLM Unlearning via Adversarial Gating Training with Adaptive Orthogonality
School of Computer Science and Technology, Xi’an Jiaotong University, China（西安交通大学计算机科学与技术学院）；MOE KLINNS Lab, Xi’an Jiaotong University, China（西安交通大学MOE KLINNS实验室）；Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, China（陕西省大数据知识工程重点实验室）
MLLMEraser: Achieving Test-Time Unlearning in Multimodal Large Language Models through Activation Steering
University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；The Hong Kong Polytechnic University（香港理工大学）；Huawei（华为）
Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning
Department of Natural Language Processing, MBZUAI.；University of Oxford.；RIKEN Center for Advanced Intelligence Project.；TMLR Group, Department of Computer Science, Hong Kong Baptist University
Beyond Superficial Unlearning: Sharpness-Aware Robust Erasure of Hallucinations in Multimodal LLMs
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；Institute for AI, Tsinghua University（清华大学人工智能研究院）；Huzhou University（湖州大学）；Institute of Dataspace, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）；University of Science and Technology of China（中国科学技术大学）
Lethe: Principled Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning
Department of Computer Science, College of Information Science and Technology, Jinan University（计算机科学系，信息科学与技术学院，暨南大学）；WMG, University of Warwick（沃森盖尔学院，沃里克大学）；School of Information and Communications Engineering, Xi’an Jiaotong University（信息与通信工程学院，西安交通大学）；Department of Computer Science, University of Warwick（计算机科学系，沃里克大学）；School of Data Science and Engineering, East China Normal University（数据科学与工程学院，华东师范大学）；School of Computer Science and Engineering, South China University of Technology（计算机科学与工程学院，华南理工大学）
ETH Zürich（苏黎世联邦理工学院）；Princeton University（普林斯顿大学）
The right to be forgotten, also known as the right to erasure, is the right of individuals to have their data erased from an entity storing it. The status of this long held notion was legally solidified recently by the General Data Protection Regulation (GDPR) in the European Union. Consequently, there is a need for mechanisms whereby users can verify if service providers comply with their deletion requests. In this work, we take the first step in proposing a formal framework to study the design of such verification mechanisms for data deletion requests -- also known as machine unlearning -- i…
TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
Stony Brook University（石英布鲁克大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Columbia University（哥伦比亚大学）；Institute of Science and Technology Austria（奥地利科学技术研究院）；Sapienza University of Rome（罗马大学 sapienza）；National University of Singapore（新加坡国立大学）
Forget and Explain: Transparent Verification of GNN Unlearning
Chung-Ang University Department of Smart Cities Seoul Republic of Korea（Chung-Ang大学智慧城市系 首尔 大韩民国）；Chung-Ang University Department of Computer Science（Chung-Ang大学计算机科学系）；Chung-Ang University（Chung-Ang大学）
Privacy laws and regulations enforce data-driven systems, e.g., recommender systems, to erase the data that concern individuals. As machine learning models potentially memorize the training data, data erasure should also unlearn the data lineage in models, which raises increasing interest in the problem of Machine Unlearning (MU). However, existing MU methods cannot be directly applied into recommendation. The basic idea of most recommender systems is collaborative filtering, but existing MU methods ignore the collaborative information across users and items. In this paper, we propose a genera…
ErrorEraser: Unlearning Data Bias for Improved Continual Learning
School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机与人工智能学院）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
Southeast University（东南大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of California, Berkeley（加州大学伯克利分校）；Nanyang Technological University（南洋理工大学）
