There are applications that may require removing the trace of a sample from the system, e.g., a user requests their data to be deleted, or corrupted data is discovered. Simply removing a sample from storage units does not necessarily remove its entire trace since downstream machine learning models may store some information about the samples used to train them. A sample can be perfectly unlearned if we retrain all models that used it from scratch with that sample removed from their training dataset. When multiple such unlearning requests are expected to be served, unlearning by retraining becomes prohibitively expensive. Ensemble learning enables the training data to be split into smaller disjoint shards that are assigned to non-communicating weak learners. Each shard is used to produce a weak model. These models are then aggregated to produce the final central model. This setup introduces an inherent trade-off between performance and unlearning cost, as reducing the shard size reduces the unlearning cost but may cause degradation in performance. In this paper, we propose a coded learning protocol where we utilize linear encoders to encode the training data into shards prior to the learning phase. We also present the corresponding unlearning protocol and show that it satisfies the perfect unlearning criterion. Our experimental results show that the proposed coded machine unlearning provides a better performance versus unlearning cost trade-off compared to the uncoded baseline.
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）
Machine Unlearning via Information Theoretic Regularization
Department of Mathematics University of California Davis（加州大学戴维斯分校数学系）；Center of Data Science and Artificial Intelligence Research University of California Davis（加州大学戴维斯分校数据科学与人工智能研究中心）
Machine unlearning refers to mechanisms that can remove the influence of a subset of training data upon request from a trained model without incurring the cost of re-training from scratch. This paper develops a unified PAC-Bayesian framework for machine unlearning that recovers the two recent design principles - variational unlearning (Nguyen et.al., 2020) and forgetting Lagrangian (Golatkar et.al., 2020) - as information risk minimization problems (Zhang,2006). Accordingly, both criteria can be interpreted as PAC-Bayesian upper bounds on the test loss of the unlearned model that take the form…
University of Toronto（多伦多大学）；Vector Institute（向量研究所）
Machine unlearning, i.e. having a model forget about some of its training data, has become increasingly more important as privacy legislation promotes variants of the right-to-be-forgotten. In the context of deep learning, approaches for machine unlearning are broadly categorized into two classes: exact unlearning methods, where an entity has formally removed the data point's impact on the model by retraining the model from scratch, and approximate unlearning, where an entity approximates the model parameters one would obtain by exact unlearning to save on compute costs. In this paper, we firs…
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）
Machine Unlearning of Pre-trained Large Language Models
University of Virginia（弗吉尼亚大学）；Georgia Institute of Technology（佐治亚理工学院）；The Hong Kong Polytechnic University（香港理工大学）；University of Melbourne（墨尔本大学）；Carnegie Mellon University（卡内基梅隆大学）
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 Toronto（多伦多大学）；Vector Institute（向量研究所）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
Once users have shared their data online, it is generally difficult for them to revoke access and ask for the data to be deleted. Machine learning (ML) exacerbates this problem because any model trained with said data may have memorized it, putting users at risk of a successful privacy attack exposing their information. Yet, having models unlearn is notoriously difficult. We introduce SISA training, a framework that expedites the unlearning process by strategically limiting the influence of a data point in the training procedure. While our framework is applicable to any learning algorithm, it …
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）
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）
Cornell University（康奈尔大学）；University of Waterloo（滑铁卢大学）；Google Research（谷歌研究院）
We study the problem of unlearning datapoints from a learnt model. The learner first receives a dataset $S$ drawn i.i.d. from an unknown distribution, and outputs a model $\widehat{w}$ that performs well on unseen samples from the same distribution. However, at some point in the future, any training datapoint $z \in S$ can request to be unlearned, thus prompting the learner to modify its output model while still ensuring the same accuracy guarantees. We initiate a rigorous study of generalization in machine unlearning, where the goal is to perform well on previously unseen datapoints. Our focu…
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）
TruVRF: Towards Triple-Granularity Verification on Machine Unlearning
School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
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）
In recent years, machine learning neural network has penetrated deeply into people's life. As the price of convenience, people's private information also has the risk of disclosure. The "right to be forgotten" was introduced in a timely manner, stipulating that individuals have the right to withdraw their consent from personal information processing activities based on their consent. To solve this problem, machine unlearning is proposed, which allows the model to erase all memory of private information. Previous studies, including retraining and incremental learning to update models, often tak…
IIIT Hyderabad（印度信息技术研究所海得拉巴分校）；ELLIS Institute Tübingen（埃利斯研究所蒂宾根分所）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Oxford（牛津大学）；Tübingen AI Center（蒂宾根人工智能中心）；University of Copenhagen（哥本哈根大学）
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
Nanjing University of Science and Technology（南京理工大学）；Xidian University（西安电子科技大学）；CSIRO（联邦科学与工业研究组织）；University of Western Australia（西澳大学）；University of Technology Sydney（悉尼科技大学）
