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The explosive growth of machine learning has made it a critical infrastructure in the era of artificial intelligence.
Online learning and stochastic approximations
Léon Bottou et al · 1998
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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Poisoning attacks against support vector machines
Biggio Battista, Blaine Nelson, and Pavel Laskov · 2012
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The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’
Alessandro Mantelero · 2013
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Generalization in adaptive data analysis and holdout reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, and Aaron Roth · 2015
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Advances in natural language processing
Julia Hirschberg and Christopher D Manning · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
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Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 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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A survey on security threats and defensive techniques of machine learning: A data driven view
Qiang Liu, Pan Li, Wentao Zhao, Wei Cai, Shui Yu, and Victor CM Leung · 2018
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Algorithms that remember: model inversion attacks and data protection law
Michael Veale, Reuben Binns, and Lilian Edwards · 2018
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Property inference attacks on fully connected neural networks using permutation invariant representations
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov · 2018
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Secure outsourced matrix computation and application to neural networks
Xiaoqian Jiang, Miran Kim, Kristin E. Lauter, and Yongsoo Song · 2018
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Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2018
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
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Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes · 2019
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Data security issues in deep learning: Attacks, countermeasures, and opportunities
Guowen Xu, Hongwei Li, Hao Ren, Kan Yang, and Robert H Deng · 2019
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Privacy attacks on network embeddings
Michael Ellers, Michael Cochez, Tobias Schumacher, Markus Strohmaier, and Florian Lemmerich · 2019
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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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A swiss army infinitesimal jackknife
Ryan Giordano, William Stephenson, Runjing Liu, Michael Jordan, and Tamara Broderick · 2019
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Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 2019
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Memguard: Defending against black-box membership inference attacks via adversarial examples
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong · 2019
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Computer vision and deep learning–based data anomaly detection method for structural health monitoring
Yuequan Bao, Zhiyi Tang, Hui Li, and Yufeng Zhang · 2019
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Lifelong anomaly detection through unlearning
Min Du, Zhi Chen, Chang Liu, Rajvardhan Oak, and Dawn Song · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
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Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 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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Empirical risk minimization in the non-interactive local model of differential privacy
Di Wang, Marco Gaboardi, Adam Smith, and Jinhui Xu · 2020
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New insights and perspectives on the natural gradient method
James Martens · 2020
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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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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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Towards probabilistic verification of machine unlearning
David Marco Sommer, Liwei Song, Sameer Wagh, and Prateek Mittal · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, and et al · 2020
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Gaoyang Liu, Yang Yang, Xiaoqiang Ma, Chen Wang, and Jiangchuan Liu · 2020
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Kongyang Chen, Yao Huang, and Yiwen Wang · 2021
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Federated unlearning: Guarantee the right of clients to forget
Leijie Wu, Song Guo, Junxiao Wang, Zicong Hong, Jie Zhang, and Yaohong Ding · 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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Continual learning and private unlearning
Bo Liu, Qiang Liu, and Peter Stone · 2022
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Federated unlearning with knowledge distillation
Chen Wu, Sencun Zhu, and Prasenjit Mitra · 2022
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Federated unlearning: How to efficiently erase a client in fl?
Anisa Halimi, Swanand Kadhe, Ambrish Rawat, and Nathalie Baracaldo · 2022
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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When machine unlearning jeopardizes privacy
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 2021
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Machine unlearning for random forests
Jonathan Brophy and Daniel Lowd · 2021
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When machine learning meets privacy: A survey and outlook
Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, and Zihuai Lin · 2021
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A survey on adversarial attacks and defences
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay · 2021
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Adaptive machine unlearning
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
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When is memorization of irrelevant training data necessary for high-accuracy learning?
Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, and Kunal Talwar · 2021
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Youngsik Yoon, Jinhwan Nam, Hyojeong Yun, Dongwoo Kim, and Jungseul Ok · 2022
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Recommendation unlearning
Chong Chen, Fei Sun, Min Zhang, and Bolin Ding · 2022
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Evaluating machine unlearning via epistemic uncertainty
Alexander Becker and Thomas Liebig · 2022
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Backdoor defense with machine unlearning
Yang Liu, Mingyuan Fan, Cen Chen, Ximeng Liu, Zhuo Ma, Li Wang, and Jianfeng Ma · 2022
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A survey on chatgpt: Ai-generated contents, challenges, and solutions
Yuntao Wang, Yanghe Pan, Miao Yan, Zhou Su, and Tom H. Luan · 2023
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A duty to forget, a right to be assured? exposing vulnerabilities in machine unlearning services
Hongsheng Hu, Shuo Wang, Jiamin Chang, Haonan Zhong, Ruoxi Sun, Shuang Hao, Haojin Zhu, and Minhui Xue · 2023
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Backdoor attack through machine unlearning
Peixin Zhang, Jun Sun, Mingtian Tan, and Xinyu Wang · 2023
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Inductive graph unlearning
Cheng-Long Wang, Mengdi Huai, and Di Wang · 2023
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Forget unlearning: Towards true data-deletion in machine learning
Rishav Chourasia and Neil Shah · 2023
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Efficient model updates for approximate unlearning of graph-structured data
Eli Chien, Chao Pan, and Olgica Milenkovic · 2023
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Certified edge unlearning for graph neural networks
Kun Wu, Jie Shen, Yue Ning, Ting Wang, and Wendy Hui Wang · 2023
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Machine unlearning of features and labels
Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck · 2023
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Deltaboost: Gradient boosting decision trees with efficient machine unlearning
Zhaomin Wu, Junhui Zhu, Qinbin Li, and Bingsheng He · 2023
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Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher
Vikram S. Chundawat, Ayush K. Tarun, Murari Mandal, and Mohan S. Kankanhalli · 2023
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Zero-shot machine unlearning
Vikram S. Chundawat, Ayush K. Tarun, Murari Mandal, and Mohan S. Kankanhalli · 2023
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Approximate data deletion in generative models
Zhifeng Kong and Scott Alfeld · 2023
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Gradient surgery for one-shot unlearning on generative model
Seohui Bae, Seoyoon Kim, Hyemin Jung, and Woohyung Lim · 2023
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Feature unlearning for generative models via implicit feedback
Saemi Moon, Seunghyuk Cho, and Dongwoo Kim · 2023
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Generative adversarial networks unlearning
Hui Sun, Tianqing Zhu, Wenhan Chang, and Wanlei Zhou · 2023
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Efficient model updates for approximate unlearning of graph-structured data
Eli Chien, Chao Pan, and Olgica Milenkovic · 2023
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Lessons learned: Defending against property inference attacks
Joshua Stock, Jens Wettlaufer, Daniel Demmler, and Hannes Federrath · 2023
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Knowledge unlearning for mitigating privacy risks in language models
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo · 2023
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Adversarial attacks and defenses in deep learning: From a perspective of cybersecurity
Shuai Zhou, Chi Liu, Dayong Ye, Tianqing Zhu, Wanlei Zhou, and Philip S. Yu · 2023
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Towards understanding and enhancing robustness of deep learning models against malicious unlearning attacks
Wei Qian, Chenxu Zhao, Wei Le, Meiyi Ma, and Mengdi Huai · 2023
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Learn to forget: Machine unlearning via neuron masking
Zhuo Ma, Yang Liu, Ximeng Liu, Jian Liu, Jianfeng Ma, and Kui Ren · 2023
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Language models are realistic tabular data generators
Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci · 2023
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Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich · 2023
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In-context unlearning: Language models as few shot unlearners
Martin Pawelczyk, Seth Neel, and Himabindu Lakkaraju · 2023
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Unlearning bias in language models by partitioning gradients
Charles Yu, Sullam Jeoung, Anish Kasi, Pengfei Yu, and Heng Ji · 2023
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Fast federated machine unlearning with nonlinear functional theory
Tianshi Che, Yang Zhou, Zijie Zhang, Lingjuan Lyu, Ji Liu, Da Yan, Dejing Dou, and Jun Huan · 2023
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Machine unlearning of federated clusters
Chao Pan, Jin Sima, Saurav Prakash, Vishal Rana, and Olgica Milenkovic · 2023
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Fedrecovery: Differentially private machine unlearning for federated learning frameworks
Lefeng Zhang, Tianqing Zhu, Haibin Zhang, Ping Xiong, and Wanlei Zhou · 2023
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Turning a curse into a blessing: Enabling in-distribution-data-free backdoor removal via stabilized model inversion
Si Chen, Yi Zeng, Won Park, Jiachen T Wang, Xun Chen, Lingjuan Lyu, Zhuoqing Mao, and Ruoxi Jia · 2023
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Machine unlearning: A survey
Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, and Philip S. Yu · 2024
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Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth · 2024
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