Have You Forgotten? A Method to Assess if Machine Learning Models Have Forgotten Data

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · 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

Federated learning: Challenges, methods, and future directions

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210 · 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

Reading digits in natural images with unsupervised feature learning

Netzer, Y. (2024) · doi:10.57702/e7wwo0zk · Cited by 4,261 · see citing works

Unbiased look at dataset bias

Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440 · see citing works

Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?

Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M. A., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2837502 · Cited by 2,431 · see citing works

Mutual Information Neural Estimation.

Belghazi, M. I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A. C., & Hjelm, D. (2018) · International Conference on Machine Learning · Cited by 920 · see citing works

The 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

Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation

Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_9 · Cited by 609 · see citing works

Privacy-Preserving Federated Brain Tumour Segmentation

Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32692-0_16 · Cited by 578 · 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

Differential Privacy and Machine Learning: a Survey and ReviewPreprint

Ji, Z., Lipton, Z. C., & Elkan, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.7584 · Cited by 239 · see citing works

On the Kolmogorov–Smirnov Limit Theorems for Empirical Distributions

Feller, W. (2015) · Springer eBooks · doi:10.1007/978-3-319-16859-3_38 · Cited by 224 · see citing works

BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning

Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N., & Wachinger, C. (2019) · arXiv · doi:10.48550/arxiv.1905.06731 · Cited by 212 · see citing works

A Survey on Differentially Private Machine Learning [Review Article]

Gong, M., Xie, Y., Pan, K., Feng, K., & Qin, A. K. (2020) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2020.2976185 · Cited by 115 · cited 1× · see citing works

F-BLEAU: Fast Black-Box Leakage Estimation

Cherubin, G., Konstantinos Chatzikokolakis, A., & Catuscia Palamidessi, A. (2019) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2019.00073 · Cited by 39 · see citing works

Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data

Glazer, A., Lindenbaum, M., & Markovitch, S. (2012) · Cited by 19 · see citing works

Federating distributed and heterogeneous information sources in neuroimaging: the NeuroBase Project.

Barillot, C., Benali, H., Dojat, M., Alban Gaignard, A., Bernard Gibaud, A., Kinkingnéhun, S., Matsumoto, J.-P., Pélégrini‐Issac, M., Simon, E., & Temal, L. (2006) · PubMed · Cited by 16 · see citing works

Under the Hood of Membership Inference Attacks on Aggregate Location Time-Series.Preprint

Pyrgelis, A., Troncoso, C., & Cristofaro, E. D. (2019) · arXiv (Cornell University) · Cited by 11 · see citing works

Measuring Membership Privacy on Aggregate Location Time-Series

Pyrgelis, A., Troncoso, C., & Cristofaro, E. D. (2020) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/3393691.3394200 · Cited by 10 · see citing works

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

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I

(2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59710-8 · Cited by 156 · see citing works

Machine Unlearning: A Survey

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

Learning disentangled representations in the imaging domain

Liu, X., Sanchez, P., Thermos, S., O’Neil, A. Q., & Tsaftaris, S. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102516 · Cited by 112 · see citing works

Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review

Xiong, P., Lee, S. M., & Chan, G. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.860032 · Cited by 104 · see citing works

Survey: Leakage and Privacy at Inference Time

Jegorova, M., Kaul, C., Mayor, C., O'Neil, A. Q., Weir, A., Murray-Smith, R., & Tsaftaris, S. A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3229593 · Cited by 77 · see citing works

Surgical data science – from concepts toward clinical translation

Maier‐Hein, L., Eisenmann, M., Sarıkaya, D., März, K., Collins, T., Malpani, A., Fallert, J., Feußner, H., Giannarou, S., Mascagni, P., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102306 · Cited by 88 · 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

Proof of Unlearning: Definitions and Instantiation

Weng, J., Yao, S., Du, Y., Huang, J., Weng, J., & Wang, C. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3358993 · Cited by 30 · see citing works

A unified method to revoke the private data of patients in intelligent healthcare with audit to forget

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

A Dataset Auditing Method for Collaboratively Trained Machine Learning Models

Huang, Y., Huang, C.-Y., Li, X., & Li, K. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3220706 · Cited by 16 · see citing works

Class Clown: Data Redaction in Machine Unlearning at Enterprise Scale

Felps, D., Schwickerath, A., Williams, J., Vuong, T., Briggs, A., Hunt, M., Sakmar, E., Saranchak, D., & Shumaker, T. (2021) · Proceedings of the 10th International Conference on Operations Research and Enterprise Systems · doi:10.5220/0010419600070014 · Cited by 12 · see citing works

Neural networks memorise personal information from one sample

Hartley, J., Sanchez, P. P., Haider, F., & Tsaftaris, S. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-48034-3 · Cited by 10 · see citing works

Attesting Distributional Properties of Training Data for Machine Learning

Duddu, V., Das, A., Khayata, N., Yalame, H., Schneider, T. H., & Asokan, N. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70879-4_1 · Cited by 6 · see citing works

Why Patient Data Cannot Be Easily Forgotten?

Su, R., Liu, X., & Tsaftaris, S. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16452-1_60 · Cited by 5 · see citing works

$$\mathsf {EMA}$$: Auditing Data Removal from Trained Models

Huang, Y., Li, X., & Li, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87240-3_76 · Cited by 4 · 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

EMA: Auditing Data Removal from Trained ModelsPreprint

Huang, Y., Li, X., & Li, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03675 · Cited by 3 · see citing works

AuditingAI: Explainability-Driven Verification of Machine Learning Models

Tripathi, A. (2025) · International Conference on Responsible, Generative and Explainable AI (ResGenXAI) · doi:10.1109/resgenxai64788.2025.11343999

Machine Unlearning

Meng, X. (2026) · Big data management · doi:10.1007/978-981-95-5787-5_9

Machine Unlearning Methodology Based on Stochastic Teacher Network

Zhang, X., Wang, J., Cheng, N., Sun, Y., Zhang, C., & Xiao, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46677-9_18 · Cited by 7 · see citing works

Fast yet versatile machine unlearning for deep neural networks

Chen, K., Zhang, D., Mi, B., Huang, Y., & Ли, Ж. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107648 · Cited by 3 · see citing works

Selective Forgetting of Deep Networks at a Finer Level than SamplesPreprint

Hayase, T., Yasutomi, S., & Katoh, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11849 · Cited by 9 · see citing works

Efficient Two-stage Model Retraining for Machine Unlearning

Kim, J., & Woo, S. S. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00482 · Cited by 46 · see citing works

Making AI Forget You: Data Deletion in Machine Learning

Ginart, A., Guan, M. Y., Valiant, G., & Zou, J. (2019) · arXiv · doi:10.48550/arxiv.1907.05012 · Cited by 175 · see citing works