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Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models.
Making AI forget you: Data deletion in machine learning
Antonio Ginart, Melody Y. Guan, Gregory Valiant, and James Zou · 1907
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann Lecun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Mixed-privacy forgetting in deep networks
Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto · 2012
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Earlier work this paper cites.
Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Max-information, differential privacy, and post-selection hypothesis testing
Ryan Rogers, Aaron Roth, Adam Smith, and Om Thakkar · 2016
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Cited alongside, same era.
Cynthia Dwork and Vitaly Feldman · 2018
Cited alongside, same era.
Mitigating bias in adaptive data gathering via differential privacy
Seth Neel and Aaron Roth · 2018
Cited alongside, same era.
Adversarially robust streaming algorithms via differential privacy
Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, and Uri Stemmer · 2020
Later among the works it cites.
Guidance on the ai auditing framework
The U.K. Information Commissioner’s Office ICO · 2020
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A new analysis of differential privacy’s generalization guarantees
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld · 2020
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2021
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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Descent-to-delete: Gradient-based methods for machine unlearning
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2021
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Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
Cited alongside, same era.
Algorithms that remember: Model inversion attacks and data protection law
Michael Veale, Reuben Binns, and Lilian Edwards · 2018
Cited alongside, same era.
Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens van der Maaten · 2019
Cited alongside, same era.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor
Cited in the paper.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith
Cited in the paper.
The reusable holdout: Preserving validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth
Cited in the paper.
Generalization in adaptive data analysis and holdout reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth
Cited in the paper.
Closest in time.
Tempered sigmoid activations for deep learning with differential privacy
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, and Ulfar Erlingsson · 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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