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Privacy regulations like the GDPR in Europe and the CCPA in the US allow users the right to remove their data ML applications.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research [best of the web]
Li Deng · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Plausible deniability for privacy-preserving data synthesis
Vincent Bindschaedler, Reza Shokri, and Carl A Gunter · 2017
Earlier work this paper cites.
Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Rényi differential privacy
Ilya Mironov · 2017
Earlier work this paper cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
Cited alongside, same era.
Revisiting membership inference under realistic assumptions
Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, and David Evans · 2020
Cited alongside, same era.
Explaining recurrent machine learning models: integral privacy revisited
Vicenç Torra, Guillermo Navarro-Arribas, and Edgar Galván · 2020
Cited alongside, same era.
Federaser: Enabling efficient client-level data removal from federated learning models
Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu · 2021
Cited alongside, same era.
Forgeability and membership inference attacks
Zhifeng Kong, Amrita Roy Chowdhury, and Kamalika Chaudhuri · 2022
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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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Subject membership inference attacks in federated learning
Anshuman Suri, Pallika Kanani, Virendra J Marathe, and Daniel W Peterson · 2022
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On the necessity of auditable algorithmic definitions for machine unlearning
Anvith Thudi, Hengrui Jia, Ilia Shumailov, and Nicolas Papernot · 2022
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Federated unlearning with knowledge distillation
Chen Wu, Sencun Zhu, and Prasenjit Mitra · 2022
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
Cited alongside, same era.
Federated unlearning: How to efficiently erase a client in fl?
Anisa Halimi, Swanand Kadhe, Ambrish Rawat, and Nathalie Baracaldo · 2022
Cited alongside, same era.
k-ipfedavg: k-anonymous integrally private federated averaging with convergence guarantee
Ayush K Varshney and Vicenc Torra
Cited in the paper.
Integrally private model selection for deep neural networks
A.K. Varshney and V. Torra · 2023
Later among the works it cites.
Federated unlearning and its privacy threats
Fei Wang, Baochun Li, and Bo Li · 2023
Later among the works it cites.