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Machine unlearning is motivated by desire for data autonomy: a person can request to have their data's influence removed from deployed models, and those models should be updated as if they were retrained without the person's data.
The influence curve and its role in robust estimation
Hampel, F. R · 1974
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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Random features for large-scale kernel machines
Rahimi, A. and Recht, B · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Privacy in pharmacogenetics: An { \{ End-to-End } \} case study of personalized warfarin dosing
Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., and Ristenpart, T · 2014
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Towards making systems forget with machine unlearning
Cao, Y. and Yang, J · 2015
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An introduction to matrix concentration inequalities
Tropp, J. A. et al · 2015
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Revisiting differentially private regression: Lessons from learning theory and their consequences
Wu, X., Fredrikson, M., Wu, W., Jha, S., and Naughton, J. F · 2015
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Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Making ai forget you: Data deletion in machine learning
Ginart, A., Guan, M., Valiant, G., and Zou, J. Y · 2019
Cited alongside, same era.
Zhu, L., Liu, Z., and Han, S · 2019
Cited alongside, same era.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
Golatkar, A., Achille, A., and Soatto, S · 2020
Cited alongside, same era.
{ \{ Updates-Leak } \} : Data set inference and reconstruction attacks in online learning
Salem, A., Bhattacharya, A., Backes, M., Fritz, M., and Zhang, Y · 2020
Cited alongside, same era.
idlg: Improved deep leakage from gradients
Zhao, B., Mopuri, K. R., and Bilen, H · 2020
Cited alongside, same era.
Descent-to-delete: Gradient-based methods for machine unlearning
Neel, S., Roth, A., and Sharifi-Malvajerdi, S · 2021
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Reconstructing training data with informed adversaries
Balle, B., Cherubin, G., and Hayes, J · 2022
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Membership inference attacks from first principles
Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., and Tramer, F · 2022
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Forget unlearning: Towards true data-deletion in machine learning
Chourasia, R., Shah, N., and Shokri, R · 2022
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Deletion inference, reconstruction, and compliance in machine (un) learning
Gao, J., Garg, S., Mahmoody, M., and Vasudevan, P. N · 2022
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Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N · 2021
Cited alongside, same era.
Extracting training data from large language models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al · 2021
Cited alongside, same era.
When machine unlearning jeopardizes privacy
Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., and Zhang, Y · 2021
Cited alongside, same era.
Retiring adult: New datasets for fair machine learning
Ding, F., Hardt, M., Miller, J., and Schmidt, L · 2021
Cited alongside, same era.
Adaptive machine unlearning
Gupta, V., Jung, C., Neel, S., Roth, A., Sharifi-Malvajerdi, S., and Waites, C · 2021
Cited alongside, same era.
Approximate data deletion from machine learning models
Izzo, Z., Smart, M. A., Chaudhuri, K., and Zou, J · 2021
Cited alongside, same era.
Rethinking influence functions of neural networks in the over-parameterized regime
Zhang, R. and Zhang, S · 2022
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Scalable membership inference attacks via quantile regression
Bertran, M., Tang, S., Kearns, M., Morgenstern, J., Roth, A., and Wu, Z. S · 2023
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Extracting training data from diffusion models
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramer, F., Balle, B., Ippolito, D., and Wallace, E · 2023
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Confidence-ranked reconstruction of census microdata from published statistics
Dick, T., Dwork, C., Kearns, M., Liu, T., Roth, A., Vietri, G., and Wu, Z. S · 2023
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Reconstructing training data from model gradient, provably
Wang, Z., Lee, J., and Lei, Q · 2023
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Learn what you want to unlearn: Unlearning inversion attacks against machine unlearning
Hu, H., Wang, S., Dong, T., and Xue, M · 2024
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