Making Machine Learning Forget

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(2014) · Cited by 33,419 · see citing works

Calibrating Noise to Sensitivity in Private Data Analysis

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Deep Learning with Differential Privacy

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Deep Face Recognition

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Membership Inference Attacks Against Machine Learning Models

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Privacy-preserving data mining

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Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

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6th SGEM International Multidisciplinary Scientific Conferences on SOCIAL SCIENCES and ARTS Proceedings, Modern Science

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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

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Privacy preserving mining of association rules

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Towards Making Systems Forget with Machine Unlearning

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Prochlo: Strong Privacy for Analytics in the Crowd

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Scalable Private Learning with PATE

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Solutions to problematic polypharmacy: learning from the expertise of patients

Reeve, J. L., Dickenson, M., Harris, J., Ranson, E., Dohnhammer, U., Cooper, L., Krska, J., Byng, R., & Britten, N. (2015) · British Journal of General Practice · doi:10.3399/bjgp15x685465 · Cited by 361 · see citing works

The U.S. Census Bureau Adopts Differential Privacy

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Algorithms that remember: model inversion attacks and data protection law

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Privacy preserving mining of association rules

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Detecting Violations of Differential Privacy

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Differentially Private Deep Learning

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A Survey of Machine Unlearning

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Machine Unlearning: A Survey

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VeriFi: Towards Verifiable Federated Unlearning

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Machine Unlearning for Random Forests

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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

FAST: Adopting Federated Unlearning to Eliminating Malicious Terminals at Server Side

Guo, X., Wang, P., Qiu, S., Song, W., Zhang, Q., Wei, X., & Zhou, D. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3343117 · Cited by 31 · 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

SSSE: Efficiently Erasing Samples from Trained Machine Learning ModelsPreprint

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Machine Unlearning Method Based On Projection Residual

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A Decision-Making Process to Implement the ‘Right to Be Forgotten’ in Machine Learning

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Why Patient Data Cannot Be Easily Forgotten?

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Predicting age and gender from network telemetry: Implications for privacy and impact on policy

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DART: Data Addition and Removal Trees.Preprint

Brophy, J., & Lowd, D. (2020) · arXiv (Cornell University) · Cited by 4 · see citing works

FedScale: A Federated Unlearning Method Mimicking Human Forgetting Processes

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Approach for Class-Wise Unlearning of Data Along with Learning of New Data

Guru, D. S., & Shivaprasad, D. L. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-18974-5_14

Amnesiac Machine Learning

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Does AI Remember? Neural Networks and the Right to be ForgottenPreprint

Graves, L. M., Nagisetty, V., & Ganesh, V. (2020) · UWSpace (University of Waterloo) · Cited by 1 · 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

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

Learn to Forget: Machine Unlearning via Neuron MaskingPreprint

Liu, Y., Ma, Z., Liu, X., Liu, J., Jiang, Z., Ma, J., Yu, P. S., & Ren, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.10933 · Cited by 4 · see citing works