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The practical needs of the ``right to be forgotten'' and poisoned data removal call for efficient \textit{machine unlearning} techniques, which enable machine learning models to unlearn, or to forget a fraction of training data and its lineage.
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Model inversion attacks that exploit confidence information and basic countermeasures
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Deep residual learning for image recognition
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Membership inference attacks against machine learning models
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The EU General Data Protection Regulation (GDPR): A Practical Guide
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Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2020
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Selective forgetting of deep networks at a finer level than samples
Tomohiro Hayase, Suguru Yasutomi, and Takashi Katoh · 2020
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Deep k-nn defense against clean-label data poisoning attacks
Neehar Peri, Neal Gupta, W Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, and John P Dickerson · 2020
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Deltagrad: Rapid retraining of machine learning models
SSSE: Efficiently erasing samples from trained machine learning models
Alexandra Peste, Dan Alistarh, and Christoph H. Lampert · 2021
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On the difficulty of membership inference attacks
Shahbaz Rezaei and Xin Liu · 2021
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Adversarial machine learning attacks and defense methods in the cyber security domain
Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, and Lior Rokach · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh · 2021
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Fast yet effective machine unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli · 2021
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Membership inference attacks on machine learning: A survey
Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Philip S Yu, and Xuyun Zhang · 2022
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Adversarial attacks and defenses: Frontiers, advances and practice
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Machine unlearning
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When machine unlearning jeopardizes privacy
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Amnesiac machine learning
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Model inversion attack by integration of deep generative models: Privacy-sensitive face generation from a face recognition system
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Your model trains on my data? protecting intellectual property of training data via membership fingerprint authentication
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The right to be forgotten in federated learning: An efficient realization with rapid retraining
Yi Liu, Lei Xu, Xingliang Yuan, Cong Wang, and Bo Li · 2022
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Deep unlearning via randomized conditionally independent hessians
Ronak Mehta, Sourav Pal, Vikas Singh, and Sathya N. Ravi · 2022
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Federated unlearning via class-discriminative pruning
Junxiao Wang, Song Guo, Xin Xie, and Heng Qi · 2022
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MLaaS in the wild: Workload analysis and scheduling in large-scale heterogeneous GPU clusters
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Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, and Tom Goldstein · 2023
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