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With privacy legislation empowering the users with the right to be forgotten, it has become essential to make a model amenable for forgetting some of its training data.
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Towards making systems forget with machine unlearning
Cao, Y. and Yang, J · 2015
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Emnist: Extending mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T., Dolan-Gavitt, B., and Garg, S · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 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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The eu general data protection regulation (gdpr)
Voigt, P. and Von dem Bussche, A · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Adversarial robustness toolbox v1.2.0
Nicolae, M.-I., Sinn, M., Tran, M. N., Buesser, B., Rawat, A., Wistuba, M., Zantedeschi, V., Baracaldo, N., Chen, B., Ludwig, H., Molloy, I., and Edwards, B · 2018
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The california consumer privacy act: Towards a european-style privacy regime in the united states
Pardau, S. L · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S · 2018
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Lifelong anomaly detection through unlearning
Du, M., Chen, Z., Liu, C., Oak, R., and Song, D · 2019
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Making ai forget you: Data deletion in machine learning
Ginart, A., Guan, M., Valiant, G., and Zou, J. Y · 2019
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Certified data removal from machine learning models
Guo, C., Goldstein, T., Hannun, A., and Van Der Maaten, L · 2019
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Machine unlearning: Linear filtration for logit-based classifiers
Baumhauer, T., Schöttle, P., and Zeppelzauer, M · 2020
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Descent-to-delete: Gradient-based methods for machine unlearning
Neel, S., Roth, A., and Sharifi-Malvajerdi, S · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Sekhari, A., Acharya, J., Kamath, G., and Suresh, A. T · 2021
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Unrolling sgd: Understanding factors influencing machine unlearning
Thudi, A., Deza, G., Chandrasekaran, V., and Papernot, N · 2021
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The right to be forgotten in federated learning: An efficient realization with rapid retraining
Liu, Y., Xu, L., Yuan, X., Wang, C., and Li, B · 2022
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A survey of machine unlearning
Nguyen, T. T., Huynh, T. T., Nguyen, P. L., Liew, A. W.-C., Yin, H., and Nguyen, Q. V. H · 2022
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Graves, L., Nagisetty, V., and Ganesh, V · 2020
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Machine unlearning
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N · 2021
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Extracting training data from large language models
Carlini, N., Tramèr, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, Ú., Oprea, A., and Raffel, C · 2021
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Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, R. G. L., Eichner, H., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konecný, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Özgür, A., Pagh, R., Qi, H., Ramage, D., Raskar, R., Raykova, M., Song, D., Song, W., Stich, S. U., Sun, Z., Suresh, A. T., Tramèr, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F. X., Yu, H., and Zhao, S · 2021
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Does BERT pretrained on clinical notes reveal sensitive data?
Lehman, E., Jain, S., Pichotta, K., Goldberg, Y., and Wallace, B · 2021
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Federaser: Enabling efficient client-level data removal from federated learning models
Liu, G., Ma, X., Yang, Y., Wang, C., and Liu, J · 2021
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Golatkar, A., Achille, A., and Soatto, S
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Federated unlearning via class-discriminative pruning
Wang, J., Guo, S., Xie, X., and Qi, H · 2022
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Federated unlearning with knowledge distillation
Wu, C., Zhu, S., and Mitra, P · 2022
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Quantifying memorization across neural language models
Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., and Zhang, C · 2023
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Knowledge unlearning for mitigating privacy risks in language models
Jang, J., Yoon, D., Yang, S., Cha, S., Lee, M., Logeswaran, L., and Seo, M · 2023
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Machine unlearning of features and labels
Warnecke, A., Pirch, L., Wressnegger, C., and Rieck, K · 2023
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Machine unlearning: A survey
Xu, H., Zhu, T., Zhang, L., Zhou, W., and Yu, P. S · 2023
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