Unrolling SGD: Understanding Factors Influencing Machine Unlearning
Open accessAbstract
Machine unlearning is the process through which a deployed machine learning model is made to forget about some of its training data points. While naively retraining the model from scratch is an option, it is almost always associated with large computational overheads for deep learning models. Thus, several approaches to approximately unlearn have been proposed along with corresponding metrics that formalize what it means for a model to forget about a data point. In this work, we first taxonomize approaches and metrics of approximate unlearning. As a result, we identify verification error, i.e., the$\ell_{2}$difference between the weights of an approximately unlearned and a naively retrained model, as an approximate unlearning metric that should be optimized for as it subsumes a large class of other metrics. We theoretically analyze the canonical training algorithm, stochastic gradient descent (SGD), to surface the variables which are relevant to reducing the verification error of approximate unlearning for SGD. From this analysis, we first derive an easy-to-compute proxy for verification error (termed unlearning error). The analysis also informs the design of a new training objective penalty that limits the overall change in weights during SGD and as a result facilitates approximate unlearning with lower verification error. We validate our theoretical work through an empirical evaluation on learning with CIFAR-10, CIFAR-100, and IMDB sentiment analysis.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578 · see citing works
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557 · see citing works
Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754 · see citing works
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01108 · Cited by 4,653 · see citing works
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856 · see citing works
Learning Word Vectors for Sentiment Analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011) · Cited by 3,710 · see citing works
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961 · see citing works
The Regression Analysis of Binary Sequences
Cox, D. R. (1958) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1958.tb00292.x · Cited by 2,909 · see citing works
Real Analysis: Modern Techniques and Their Applications
Folland, G. B. (1984) · Cited by 2,294 · see citing works
Stochastic Estimation of the Maximum of a Regression Function
Kiefer, J., & Wolfowitz, J. (1952) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729392 · Cited by 2,200 · see citing works
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., & Jha, S. (2018) · 2018 IEEE 31st Computer Security Foundations Symposium (CSF) · doi:10.1109/csf.2018.00027 · Cited by 1,001 · see citing works
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., & Papernot, N. (2021) · 2021 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp40001.2021.00019 · Cited by 810 · see citing works
Self-citationThe Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., & Song, D. (2018) · arXiv · doi:10.48550/arxiv.1802.08232 · Cited by 629 · see citing works
Towards Making Systems Forget with Machine Unlearning
Cao, Y., & Yang, J. (2015) · 2015 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2015.35 · Cited by 622 · cited 1× · see citing works
“[6] to unlearn datapoints for simple hypothesis spaces which have known SQ learning algorithms.”
BackgroundFast Exact Multiplication by the Hessian
Pearlmutter, B. A. (1994) · Neural Computation · doi:10.1162/neco.1994.6.1.147 · Cited by 553 · see citing works
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks
Golatkar, A., Achille, A., & Soatto, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00932 · Cited by 394 · see citing works
Overview of Supervised Learning
Hastie, T., Friedman, J. H., & Tibshirani, R. J. (2001) · Springer series in statistics · doi:10.1007/978-0-387-21606-5_2 · Cited by 333 · see citing works
DeltaGrad: Rapid retraining of machine learning modelsPreprint
Wu, Y., Dobriban, E., & Davidson, S. (2020) · International Conference on Machine Learning · doi:10.48550/arxiv.2006.14755 · Cited by 278 · see citing works
Mantelero, A. (2013) · Computer Law & Security Review · doi:10.1016/j.clsr.2013.03.010 · Cited by 264 · see citing works
Overview of Supervised Learning
Hastie, T., Tibshirani, R. J., & Friedman, J. (2008) · Springer series in statistics · doi:10.1007/978-0-387-84858-7_2 · Cited by 258 · see citing works
When Machine Unlearning Jeopardizes Privacy
Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., & Zhang, Y. (2021) · Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3460120.3484756 · Cited by 195 · cited 1× · see citing works
“Machine unlearning has since been extended to deep neural networks (DNNs) [7, 8, 9, 10, 11, 12, 13], and its privacy implications have been studied [14, 15, 16].”
BackgroundSystematic Evaluation of Privacy Risks of Machine Learning ModelsPreprint
Song, L., & Mittal, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.10595 · Cited by 185 · see citing works
Certified Data Removal from Machine Learning Models
Guo, C., Goldstein, T., Hannun, A., & van der Maaten, L. (2019) · arXiv · doi:10.48550/arxiv.1911.03030 · Cited by 160 · see citing works
Mathematical analysis: an introduction
(1996) · Choice Reviews Online · doi:10.5860/choice.33-5765 · Cited by 142 · see citing works
Machine unlearning: linear filtration for logit-based classifiers
Baumhauer, T., Schöttle, P., & Zeppelzauer, M. (2022) · Machine Learning · doi:10.1007/s10994-022-06178-9 · Cited by 123 · see citing works
Golatkar, A., Achille, A., & Soatto, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58526-6_23 · Cited by 122 · see citing works
Machine Unlearning: Linear Filtration for Logit-based Classifiers
Baumhauer, T., Schöttle, P., & Zeppelzauer, M. (2020) · arXiv · doi:10.48550/arxiv.2002.02730 · Cited by 110 · see citing works
Remember What You Want to Forget: Algorithms for Machine Unlearning
Sekhari, A., Acharya, J., Kamath, G., & Suresh, A. T. (2021) · arXiv · doi:10.48550/arxiv.2103.03279 · Cited by 99 · see citing works
Proof-of-Learning: Definitions and Practice
Jia, H., Yaghini, M., Choquette-Choo, C. A., Dullerud, N., Thudi, A., Chandrasekaran, V., & Papernot, N. (2021) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp40001.2021.00106 · Cited by 89 · see citing works
Self-citationAnalyzing Information Leakage of Updates to Natural Language Models
Zanella-Béguelin, S., Wutschitz, L., Tople, S., Rühle, V., Paverd, A., Ohrimenko, O., Köpf, B., & Brockschmidt, M. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3372297.3417880 · Cited by 86 · see citing works
Gupta, V., Jung, C., Neel, S., Roth, A., Sharifi-Malvajerdi, S., & Waites, C. (2021) · arXiv · doi:10.48550/arxiv.2106.04378 · Cited by 67 · see citing works
Graves, L., Nagisetty, V., & Ganesh, V. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i13.17371 · Cited by 5 · see citing works
Proof-of-Learning: Definitions and PracticePreprint
Jia, H., Yaghini, M., Choquette-Choo, C. A., Dullerud, N., Thudi, A., Chandrasekaran, V., & Papernot, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.05633 · Cited by 5 · see citing works
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Flagged first, then by citations
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A Survey of Machine Unlearning
Nguyên, T. T., Huynh, T. T., Ren, Z. X., Nguyen, P. L., Liew, A. W., Yin, H., & Nguyen, Q. V. H. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3749987 · Cited by 303 · see citing works
Xu, H., Zhu, T., Zhang, L., Zhou, W., & Yu, P. S. (2024) · ACM Computing Surveys · doi:10.1145/3603620 · Cited by 164 · see citing works
“[37] discussed the semantics behind unlearning and reviewed existing unlearning schemes based on logits, weights, and weight distributions.”
BackgroundMachine Unlearning: Solutions and Challenges
Xu, J., Wu, Z., Wang, C., & Jia, X. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3379240 · Cited by 114 · see citing works
Rethinking machine unlearning for large language models
Liu, S., Yao, Y., Jia, J., Casper, S., Baracaldo, N., Hase, P., Yao, Y., Liu, C. Y., Xu, X., Li, H., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-00985-0 · Cited by 173 · see citing works
A Review on Machine Unlearning
Zhang, H., Nakamura, T., Isohara, T., & Sakurai, K. (2023) · SN Computer Science · doi:10.1007/s42979-023-01767-4 · Cited by 81 · see citing works
A Survey of Machine Unlearning
Nguyen, T. T., Huynh, T. T., Ren, Z., Nguyen, P. L., Liew, A. W.-C., Yin, H., & Nguyen, Q. V. H. (2022) · arXiv · doi:10.48550/arxiv.2209.02299 · Cited by 193 · see citing works
Zhang, Y., Jia, J., Chen, X., Chen, A., Zhang, Y., Liu, S., Ding, K., & Liu, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72998-0_22 · Cited by 68 · see citing works
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
Shaik, T., Tao, X., Xie, H., Li, L., Zhu, X., & Li, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3486109 · Cited by 78 · see citing works
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning
Thudi, A., Jia, H., Shumailov, I., & Papernot, N. (2021) · arXiv · doi:10.48550/arxiv.2110.11891 · Cited by 48 · see citing works
Self-citationLearn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning
Hu, H., Wang, S., Dong, T., & Xue, M. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00248 · Cited by 44 · see citing works
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
Li, N., Zhou, C., Gao, Y., Chen, H., Zhang, Z., Kuang, B., & Fu, A. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3530988 · Cited by 53 · see citing works
Backdoor Attacks via Machine Unlearning
Liu, Z., Wang, T., Huai, M., & Miao, C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i13.29321 · Cited by 32 · see citing works
Recommendation Unlearning via Influence Function
Zhang, Y., Hu, Z., Bai, Y., Wu, J., Wang, Q., & Feng, F. (2024) · ACM Transactions on Recommender Systems · doi:10.1145/3701763 · Cited by 35 · see citing works
A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services
Hu, H., Wang, S., Chang, J., Zhong, H., Sun, R., Hao, S., Zhu, H., & Xue, M. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24252 · Cited by 37 · see citing works
Hoang, T., Rana, S., Gupta, S., & Venkatesh, S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00475 · Cited by 23 · see citing works
Zhou, J., Li, H., Liao, X., Zhang, B., He, W., Li, Z., Zhou, L., & Gao, X. (2023) · Nature Communications · doi:10.1038/s41467-023-41703-x · Cited by 22 · see citing works
An overview of machine unlearning
Li, C., Jiang, H., Chen, J., Zhao, Y. L., Fu, S., Jing, F., & Guo, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100254 · Cited by 21 · see citing works
Artificial intelligence security and privacy: a survey
He, X., Xu, G., Han, X., Wang, Q., Zhao, L., Shen, C., Lin, C., Zhao, Z., Li, Q., Yang, L., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-025-4388-5 · Cited by 23 · see citing works
Supporting Trustworthy AI Through Machine Unlearning
Hine, E., Novelli, C., Taddeo, M., & Floridi, L. (2024) · Science and Engineering Ethics · doi:10.1007/s11948-024-00500-5 · Cited by 19 · see citing works
NoT: Federated Unlearning via Weight Negation
Khalil, Y. H., Brunswic, L. M., Lamghari, S., Li, X., Beitollahi, M., & Chen, X. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02399 · Cited by 16 · see citing works
MUter: Machine Unlearning on Adversarially Trained Models
Liu, J., Xue, M., Lou, J., Zhang, X., Xiong, L., & Qin, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00451 · Cited by 16 · see citing works
Learning to Unlearn for Robust Machine Unlearning
Huang, H., Foo, L. G., & Liu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72943-0_12 · Cited by 16 · see citing works
Verifiable and Provably Secure Machine Unlearning
Eisenhofer, T., Riepel, D., Chandrasekaran, V., Ghosh, E., Ohrimenko, O., & Papernot, N. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00033 · Cited by 16 · see citing works
Self-citationMachine Unlearning in Gradient Boosting Decision Trees
Lin, H., Chung, J. W., Lao, Y., & Zhao, W. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599420 · Cited by 15 · see citing works
SAFE: Machine Unlearning With Shard Graphs
Dukler, Y., Bowman, B., Achille, A., Golatkar, A., Swaminathan, A. D., & Soatto, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01569 · Cited by 15 · see citing works
Kumar, R., Prakash, R., Kumar, N., Chinara, S., & Mohammed, M. A. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10436-z · Cited by 14 · see citing works
IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
Dong, Y., Zhang, B., Lei, Z., Zou, N., & Li, J. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671744 · Cited by 14 · see citing works
Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning
Fan, C., Liu, J., Hero, A. O., & Liu, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72664-4_16 · Cited by 14 · see citing works
Heterogeneous decentralised machine unlearning with seed model distillation
Ye, G., Chen, T., Nguyen, Q. V. H., & Yin, H. (2024) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12281 · Cited by 13 · see citing works
Machine Unlearning in Learned Databases: An Experimental Analysis
Kurmanji, M., Triantafillou, E., & Triantafillou, P. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3639304 · Cited by 13 · see citing works
Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy
Hayes, J., Shumailov, I., Triantafillou, E., Khalifa, A. M., & Papernot, N. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00034 · Cited by 14 · see citing works
Self-citationXia, X., Wang, Z., Sun, R., Liu, B., Khalil, I., & Xue, M. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00095 · Cited by 13 · see citing works
A Systematic Literature Review of Machine Unlearning Techniques in Neural Networks
Cevallos, I., Benalcázar, M. E., Caraguay, Á. L. V., Zea, J. A., & López, L. I. B. (2025) · Computers · doi:10.3390/computers14040150 · Cited by 10 · see citing works
Position: LLM Unlearning Benchmarks are Weak Measures of Progress
Thaker, P., Hu, S., Kale, N., Maurya, Y., Wu, Z. S., & Smith, V. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00035 · Cited by 15 · see citing works
GDR-GMA: Machine Unlearning via Direction-Rectified and Magnitude-Adjusted Gradients
Lin, S., Zhang, X., Susilo, W., Chen, X., & Liu, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680775 · Cited by 8 · see citing works
Speed up Federated Unlearning With Temporary Local Models
Ameen, M., Wang, P., Su, W., Wei, X., & Zhang, Q. (2025) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2025.3549112 · Cited by 8 · see citing works
Decoupled Distillation to Erase: A General Unlearning Method for Any Class-centric Tasks
Zhou, Y., Zheng, D., Mo, Q., Lu, R., Lin, K.-Y., & Zheng, W. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01895 · Cited by 8 · see citing works
Modeling and Understanding Uncertainty in Medical Image Classification
Chen, A., Li, Y., Qian, W., Morse, K., Miao, C., & Huai, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72117-5_52 · Cited by 7 · see citing works
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Wu, J., & Harandi, M. T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72970-6_21 · Cited by 7 · see citing works
Zero-Shot Class Unlearning in CLIP with Synthetic Samples
Kravets, A., & Namboodiri, V. P. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00629 · Cited by 7 · see citing works
TruVRF: Toward Triple-Granularity Verification on Machine Unlearning
Zhou, C., Gao, Y., Fu, A., Chen, K., Zhang, Z., Xue, M., Dai, Z., Ji, S., & Zhang, Y. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3565991 · Cited by 7 · see citing works
Machine Unlearning by Reversing the Continual Learning
Zhang, Y., Lu, Z., Zhang, F., Wang, H., & Li, S. (2023) · Applied Sciences · doi:10.3390/app13169341 · Cited by 7 · see citing works
A survey on large language models unlearning: taxonomy, evaluations, and future directions
Le-Khac, U. N., & Truong, V. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11376-7 · Cited by 9 · see citing works
Conditional Matching GAN Guided Reconstruction Attack in Machine Unlearning
Zhang, K., Wang, W., Fan, Z., Song, X., & Yu, S. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437231 · Cited by 6 · see citing works
Active forgetting via influence estimation for neural networks
Meng, X., Yang, Y., Liu, X., & Jiang, N. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22981 · Cited by 6 · see citing works
Towards Natural Machine Unlearning
He, Z., Li, T., Cheng, X., Huang, Z., & Huang, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3597350 · Cited by 5 · see citing works
Model Inversion Attack Against Federated Unlearning
Zhou, L., Zhu, Y., & Liu, R. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3666295 · Cited by 4 · see citing works
Tong, Y., Wang, Y., Yuan, J., & Hu, C. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01916 · Cited by 4 · see citing works
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Seo, S., Kim, D.-W., & Han, B. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00317 · Cited by 4 · see citing works
Pseudo-Labeling for Enhanced User Privacy in Approximate Machine Unlearning
Watanabe, S. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890795 · Cited by 3 · see citing works
Silver Linings in the Shadows: Harnessing Membership Fingerprinting for Machine Unlearning
Sula, N., Kumar, A., Wang, H., Hou, J., & Tourani, R. (2025) · IEEE Conference on Communications and Network Security · doi:10.1109/cns66487.2025.11195030 · Cited by 3 · see citing works
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Zhang, S., Lou, J., Xiong, L., Zhang, X., & Liu, J. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614811 · Cited by 3 · see citing works
Ensuring User Privacy and Model Security via Machine Unlearning: A Review
Tang, Y., Cai, Z., Liu, Q., Zhou, T., & Ni, Q. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.032307 · Cited by 3 · see citing works
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Zhu, W., Jia, J., Luo, B., & Lin, X. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10622238 · Cited by 3 · see citing works
ReVeil: Unconstrained Concealed Backdoor Attack on Deep Neural Networks using Machine Unlearning
Alam, M., Lamri, H., & Maniatakos, M. (2025) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac63849.2025.11133199 · Cited by 3 · see citing works
Learning to Unlearn While Retaining: Combating Gradient Conflicts in Machine Unlearning
Patel, G., & Qiu, Q. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00401 · Cited by 2 · see citing works
Data protection in the era of large language models: challenges and opportunities
Revesai, Z., & Chipfumbu, C. T. (2026) · AI and Ethics · doi:10.1007/s43681-026-01223-y · Cited by 5 · see citing works
Wang, M., Cao, C., Wang, X., Zhang, Q., Jing, Z., Li, H., & Sun, J. (2023) · Mathematics · doi:10.3390/math11214547 · Cited by 2 · see citing works
IDMU: Impact Driven Machine Unlearning
Asthana, S., Zhang, B., Mahindru, R., Banipal, I. S., & Chowdhary, P. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386841 · Cited by 2 · see citing works
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Doughan, Z. F., & Itani, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-62495-7_42 · Cited by 2 · see citing works
MUSO: achieving exact machine unlearning in over-parameterized regimes
Yang, R., He, M., He, Z., Qiu, Y., & Huang, X. (2025) · Machine Learning · doi:10.1007/s10994-025-06806-0 · Cited by 1 · see citing works
Aliyeva, L., Abdullayev, N., Zarbiyeva, S., Huseynov, I., & Suleymanov, U. (2024) · IEEE International Conference on Application of Information and Communication Technologies (AICT) · doi:10.1109/aict61888.2024.10740413 · Cited by 1 · see citing works
Efficient unlearning for data security in deep learning systems
Guo, E., Su, C., & Li, P. (2025) · The Computer Journal · doi:10.1093/comjnl/bxaf031 · Cited by 1 · see citing works
Fed$n$nP: Federated Unlearning With Multiple Client Set Partitions
Jia, J., Zhu, W., Luo, B., Lin, X., & Ma, L. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3586441 · Cited by 1 · see citing works
Federated meta unlearning based on model decomposition and weighted aggregation
Wang, Y., Fan, Q., Pan, Z., Ying, Z., Zhang, Z., & Zhu, L. (2025) · Scientia Sinica Informationis · doi:10.1360/ssi-2025-0221 · Cited by 1 · see citing works
A2E: Black-Box Anti-Adversarial Example Based Watermarking to Verify Federated Unlearning
Gao, X., Wang, J., Zhang, Z., Chen, J., Cheng, P., & Chen, J. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3598987 · Cited by 1 · see citing works
Towards Unveiling Predictive Uncertainty Vulnerabilities in the Context of the Right to Be Forgotten
Qian, W., Zhao, C., Li, Y., Ye, W., & Huai, M. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760964 · Cited by 2 · see citing works
Federated Unlearning with Oriented Saliency Compression
Xiao, B., Liu, S., & Ling, Q. H. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228643 · Cited by 1 · see citing works
Esquecer é Preciso: Um Estudo sobre o Impacto da Remoção de Dados no Desaprendizado de Máquinas
Santos, M. C., Gonçalves, J. P. D. B., Rocha, A. A. D. A., & Villaça, R. D. S. (2025) · Escola Regional de Informática do Espírito Santo (ERI-ES) · doi:10.5753/eries.2025.15735 · Cited by 1 · see citing works
Forget Me If You Can: Auditing User Data Revocation in Recommendation Systems
Zhu, Z., Yang, Y., Fan, Y., & Lian, D. (2026) · Information Systems Research · doi:10.1287/isre.2024.1179 · Cited by 1 · see citing works
Towards Practical LLM Unlearning: Efficient, Modular, and Retain-Free
Liu, P., Zhang, P.-F., Qu, J., Li, X., Li, Z., & Zhao, P. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792324 · Cited by 1 · see citing works
Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design
Sun, Y. W., Zhang, Y., Liu, G., Xie, H., & Liu, S. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00233 · Cited by 1 · see citing works
Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting
Kravets, A., Chen, D., & Namboodiri, V. P. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00185 · Cited by 1 · see citing works
Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy
Xiao, Y., Ye, Q., Hu, L., Zheng, H., Hu, H., Liang, Z., Li, H., & Jiao, Y. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00293 · Cited by 1 · see citing works
Deconstruct Evaluate and Targeted Intervention Based LLMs Unlearning Algorithm
Wang, Y., Miao, K., Hu, Y., Li, X., & Qin, Z. (2026) · Chinese Journal of Electronics · doi:10.23919/cje.2025.00.105 · Cited by 1 · see citing works
FedSSU: flexible and efficient decentralized unlearning for federated learning
Leng, Y., Xu, L., Liu, J., Zhang, X., Mei, L., Qu, Y., & Xu, C. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07478-2 · Cited by 1 · see citing works
Machine Unlearning for Foundation Models
Chen, P., & Liu, S. (2025) · Springer eBooks · doi:10.1007/978-3-031-76770-8_11 · Cited by 1 · see citing works
Efficient Machine Unlearning by Model Splitting and Core Sample Selection
Egger, M., Bitar, R., & Urbanke, R. L. (2025) · IEEE Information Theory Workshop (ITW) · doi:10.1109/itw62417.2025.11240389 · Cited by 1 · see citing works
Can Federated Models Be Rectified Through Learning Negative Gradients?
Tahir, A., Tan, Z., & Babaagba, K. O. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-52265-9_2
Selective Noise-Aided Machine Unlearning with Deep Feature Visualization
Shaheryar, M., Lee, J. T., & Jung, S. K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-77389-1_8
An Efficient Federated Meta Unlearning Algorithm with Enhanced Privacy Protection
Wang, Y., Ying, Z., Pan, Z., Zhu, E., & Zhou, W. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-2864-3_9
Gradual Negative Matching for LLM Unlearning
Kulkarni, H., Goharian, N., & Frieder, O. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88714-7_16
Periodic Recovery From Poisoning Attacks in Machine Learning
Hu, Y., Fang, M., Jia, Y., Liu, H., & Gong, N. Z. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3560239
PIU-FR: Personal Identity Unlearning for Facial Recognition
Lê, T. H., Tran, P. T., & Nguyen, N. M. B. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-5884-8_8
Towards Memory-Efficient and Sustainable Machine Unlearning on Edge using Zeroth-Order Optimizer
Ci, Z., Yang, C., Tan, Q., Liu, J., Li, A., Wang, Y., Lü, J., Wang, J., & Yuan, G. (2025) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3716368.3735273
From Expansion to Retraction: Long-tailed Machine Unlearning via Boundary Manipulation
Chen, M., Gao, W., Wang, C., Liu, G., Abdelmoniem, A. M., & Peng, K. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736970
PAMUS: An Entropy-Loss-Based Poisoning Attack for Undermining Machine Unlearning
Jiang, X., Ma, Y., Xu, W., & Wen, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00828-2_7
Copyright Protection of General Information via Simulation Task Supervision
Hu, C., Hu, Y., Li, H., Hu, Y., & Zhu, N. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3638667
Ong, W. K., & Chan, C. S. (2025) · IEEE Access · doi:10.1109/access.2025.3611992
Stimulating Catastrophic Forgetting in Class-Wise Unlearning via UAP
Zhou, W., Cheng, X., Wu, Y., Yang, R., & Huang, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06096-9_25
Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning
Zeng, Z., Gu, L., Duan, Z., Li, F., Snoek, C. G. M., Wang, M., & Shi, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37447-9_29
Adaptive Adversarial Unlearning FrameworkPreprint
soleiman, A., Sonbol, R., & Aljoumaa, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5900872
Adaptive Adversarial Unlearning FrameworkPreprint
soleiman, A., Sonbol, R., & Aljoumaa, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5900591
Improving Unlearning with Model Updates Probably Aligned with Gradients
Dine, V., Furon, T., & Faure, C. (2025) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3733799.3762975
GAIA-UL: Surgical Unlearning of Visual Knowledge via Causally-Guided Orthogonalization
Xu, J., Liu, X., Xie, X., Zhang, K., Cai, Q., Tong, X., Gong, Z., & Qu, W. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11323156
Feature Machine Unlearning in Diffusion Models
Wang, L., Zhu, T., Qin, L., Yin, L., & Zhou, W. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6419-4_18
CelebCaption: A Benchmark Dataset for Identity-Sensitive Unlearning in Image Captioning
Moon, H., & Woo, S. S. (2026) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3773966.3779359
CAUA: A Realistic and Effective Attack on Machine Unlearning Under Limited Information
Zhang, J., Zhang, Y., Cui, J., Xie, X., Fan, C., & Li, B. (2025) · Annual Computer Security Applications Conference · doi:10.1109/acsac67867.2025.00080
Adversarial Retain-Free Unlearning for Bearing Prognostics and Health Management
Yoon, C., Lee, J., & Kim, H. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3664210
CURE: Centroid-guided Unsupervised Representation Erasure for Facial Recognition Systems
Shivam, F., Najafzadeh, N., Reddy, Y. V. R., & Gyawali, P. (2025) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb65343.2025.11410631
- Authors
- Thudi, A., Deza, G., Chandrasekaran, V., & Papernot, N.
- Affiliations
- University of Toronto (CA)
- University of Wisconsin–Madison (US)
- Venue
- 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 303-319
- OpenAlex
- W3202838631
- Cited by
- 135
- References
- 38
- Citations by year
- 2026: 45 · 2025: 54 · 2024: 22 · 2023: 12 · 2022: 1 · 2021: 1
- FWCI
- 12.30
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE European Symposium on Security and Privacy (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- DARPA
- NSERC
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- machine unlearning, approximate unlearning, stochastic gradient descent, weight change, deep learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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