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 for classification models (e.g. deep neural networks). As a first step, we propose linear filtration as a intuitive, computationally efficient sanitization method. Our experiments demonstrate benefits in an adversarial setting over naive deletion schemes.
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）
CBD: API-Only LLM Black-Box Unlearning through Controlled Behavioral Divergence
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（北京未来区块链与隐私计算高精尖创新中心）；Department of Electronic and Electrical Engineering, Sungkyunkwan University（成均馆大学电子与电气工程系）
Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam.（信息科技学院，科学大学，胡志明市，越南）；Vietnam National University, Ho Chi Minh City, Vietnam.（越南国家大学，胡志明市，越南）
Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning
Tsinghua University（清华大学）；The Hong Kong University of Science and Technology（香港科技大学）；Jianghuai Advanced Technology Center（江淮先进技术中心）；University of Technology Sydney（悉尼大学）
