As the demand for user privacy grows, controlled data removal (machine unlearning) is becoming an important feature of machine learning models for data-sensitive Web applications such as social networks and recommender systems. Nevertheless, at this point it is still largely unknown how to perform efficient machine unlearning of graph neural networks (GNNs); this is especially the case when the number of training samples is small, in which case unlearning can seriously compromise the performance of the model. To address this issue, we initiate the study of unlearning the Graph Scattering Transform (GST), a mathematical framework that is efficient, provably stable under feature or graph topology perturbations, and offers graph classification performance comparable to that of GNNs. Our main contribution is the first known nonlinear approximate graph unlearning method based on GSTs. Our second contribution is a theoretical analysis of the computational complexity of the proposed unlearning mechanism, which is hard to replicate for deep neural networks. Our third contribution are extensive simulation results which show that, compared to complete retraining of GNNs after each removal request, the new GST-based approach offers, on average, a 10.38x speed-up and leads to a 2.6% increase in test accuracy during unlearning of 90 out of 100 training graphs from the IMDB dataset (10% training ratio). Our implementation is available online at https://doi.org/10.5281/zenodo.7613150.
Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
St. Pölten University of Applied Sciences（圣帕尔滕应用科技大学）；Management Center Innsbruck（因斯布鲁克管理中心）
Recently enacted legislation grants individuals certain rights to decide in what fashion their personal data may be used, and in particular a "right to be forgotten". This poses a challenge to machine learning: how to proceed when an individual retracts permission to use data which has been part of the training process of a model? From this question emerges the field of machine unlearning, which could be broadly described as the investigation of how to "delete training data from models". Our work complements this direction of research for the specific setting of class-wide deletion requests fo…
Automatic Construction of Pattern Classifiers Capable of Continuous Incremental Learning and Unlearning Tasks Based on Compact-Sized Probabilistic Neural Network
Department of Computer Engineering, College of Science & Technology（计算机工程系，科学与技术学院）
Unlearning Evaluation through Subset Statistical Independence
University of Queensland（昆士兰大学）；University of Melbourne（墨尔本大学）；Adelaide University（阿德莱德大学）；RIKEN Center for Advanced Intelligence Project（日本理化学研究所先进智能项目中心）
Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness
Provable Responsible AI and Data Analytics Lab（可证负责任人工智能与数据 analytics 实验室）；King Abdullah University of Science and Technology（卡塔尔国王 Abdullah 科学与技术大学）；National University of Singapore（新加坡国家大学）；Johns Hopkins University（约翰霍普金斯大学）
本文提出 IAM 框架，通过插值模型在样本上的泛化—拟合差距度量样本级遗忘完整性，可低成本扩展至 LLM，并揭示近似遗忘中过度与不足遗忘风险。
Griffith University（格里菲斯大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Bremen（不来梅大学）；Hanoi University of Science and Technology（河内科技大学）；The University of Queensland（昆士兰大学）
Distribution-Guided and Constrained Quantum Machine Unlearning
Department of Electrical Engineering, Lahore University of Management Sciences, Lahore 54792, Pakistan.（电气工程系，拉合尔管理科学大学，巴基斯坦拉合尔）；Department of Physics, Lahore University of Management Sciences, Lahore 54792, Pakistan.（物理系，拉合尔管理科学大学，巴基斯坦拉合尔）
Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols
Department of Artificial Intelligence, Korea University, South Korea（韩国大学人工智能系）；Computer Science Dept. at Courant Institute of Mathemtical Sciences, New York University, United States（纽约大学Courant数学科学研究所计算机科学系）
Machine Unlearning Meets Adversarial Robustness via Constrained Interventions on LLMs
Department of Computer Science, Université Paris-Dauphine（巴黎-索邦大学计算机科学系）；Institute of Mathematics, EPFL（苏黎世联邦理工学院数学研究所）；Computational Engineering Lab, Empa（Empa计算工程实验室）
Rethinking Machine Unlearning for Large Language Models
Michigan State University（密歇根州立大学）；IBM Research（IBM研究院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；ByteDance Research（字节跳动研究院）；Stanford University（斯坦福大学）
Computer Science Department, Vanderbilt University（范德比尔特大学计算机科学系）；Computer Science Department, Information Technology University（信息技术大学计算机科学系）；Systems Engineering Department, University of Texas at Dallas（德克萨斯大学达拉斯分校系统工程系）
Forgetting-MarI: LLM Unlearning via Marginal Information Regularization
Department of Mathematics, University of California Davis, USA（加州大学戴维斯分校数学系）；SLAC National Accelerator Laboratory, Stanford University, USA（斯坦福大学SLAC国家加速器实验室）；Department of Computer Science, University of California Davis, USA（加州大学戴维斯分校计算机科学系）
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA)（Vicomtech基金会，巴斯克研究与技术联盟）；Institute of Smart Cities, Public University of Navarre（纳瓦拉公立大学智慧城市研究所）
In the recent years, cybersecurity has gained high relevance, converting the detection of attacks or intrusions into a key task. In fact, a small breach in a system, application, or network, can cause huge damage for the companies. However, when this attack detection encounters the Artificial Intelligence paradigm, it can be addressed using high-quality classifiers which often need high resource demands in terms of computation or memory usage. This situation has a high impact when the attack classifiers need to be used with limited resourced devices or without overloading the performance of th…
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
University of Southern Queensland（南昆士兰大学）；Lingnan University（岭南大学）；Wuhan University of Technology（武汉理工大学）；University of Electronic Science and Technology of China（电子科技大学）；Hong Kong Polytechnic University（香港理工大学）
本文系统综述机器遗忘的最新方法、指标、数据集与挑战，并指出其在隐私、安全、公平及可信 AI 中的未来方向。
The University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）
The right to be forgotten has been legislated in many countries, but its enforcement in the AI industry would cause unbearable costs. When single data deletion requests come, companies may need to delete the whole models learned with massive resources. Existing works propose methods to remove knowledge learned from data for explicitly parameterized models, which however are not appliable to the sampling-based Bayesian inference, i.e., Markov chain Monte Carlo (MCMC), as MCMC can only infer implicit distributions. In this paper, we propose the first machine unlearning algorithm for MCMC. We fir…
SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所智能信息处理实验室）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Machine unlearning has great significance in guaranteeing model security and protecting user privacy. Additionally, many legal provisions clearly stipulate that users have the right to demand model providers to delete their own data from training set, that is, the right to be forgotten. The naive way of unlearning data is to retrain the model without it from scratch, which becomes extremely time and resource consuming at the modern scale of deep neural networks. Other unlearning approaches by refactoring model or training data struggle to gain a balance between overhead and model usability. In…
FiCABU: A Fisher-Based, Context-Adaptive Machine Unlearning Processor for Edge AI
School of Intelligent Semiconductor Engineering, Chung-Ang University（智能半导体工程学院， Chung-Ang 大学）；AI Edge SoC Research Section, the Electronics and Telecommunications Research Institute（AI 边缘 SoC 研究部门，电子电信研究所）
