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Differential privacy in the 40th international colloquium on automata
C Dwork · 2006
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Spam filtering with naive bayes-which naive bayes?
Vangelis Metsis, Ion Androutsopoulos, and Georgios Paliouras · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and C. J. Burges · 2010
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Contributions to the study of sms spam filtering: new collection and results
Tiago A Almeida, José María G Hidalgo, and Akebo Yamakami · 2011
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
Giuseppe Ateniese, Luigi V Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali, and Giovanni Felici · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Deep neural networks with random gaussian weights: A universal classification strategy?
Raja Giryes, Guillermo Sapiro, and Alex M Bronstein · 2016
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Semi-supervised knowledge transfer for deep learning from private training data, 18/10/2016
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
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Why google stores billions of lines of code in a single repository
Rachel Potvin and Josh Levenberg · 2016
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Prochlo: Strong privacy for analytics in the crowd
Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes, and Bernhard Seefeld · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Deep models under the gan: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 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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Privacy-preserving deep learning: Revisited and enhanced
Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai · 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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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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Modern code review: a case study at google
Caitlin Sadowski, Emma Söderberg, Luke Church, Michal Sipko, and Alberto Bacchelli · 2018
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, Brendan McMahan, et al · 2019
Cited alongside, same era.
Amplification by shuffling: From local to central differential privacy via anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta · 2019
Cited alongside, same era.
Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano de Cristofaro, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
Cited alongside, same era.
The skellam mechanism for differentially private federated learning
Naman Agarwal, Peter Kairouz, and Ziyu Liu · 2021
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When the curious abandon honesty: Federated learning is not private
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, and Nicolas Papernot · 2021
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Rofl: Attestable robustness for secure federated learning
Lukas Burkhalter, Hidde Lycklama, Alexander Viand, Nicolas Küchler, and Anwar Hithnawi · 2021
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Robbing the fed: Directly obtaining private data in federated learning with modified models
Liam H Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, and Tom Goldstein · 2021
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Shuffled model of differential privacy in federated learning
Antonious Girgis, Deepesh Data, Suhas Diggavi, Peter Kairouz, and Ananda Theertha Suresh · 2021
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A hybrid approach to privacy-preserving federated learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou · 2019
Cited alongside, same era.
Beyond inferring class representatives: User-level privacy leakage from federated learning
Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi · 2019
Cited alongside, same era.
Verifynet: Secure and verifiable federated learning
Guowen Xu, Hongwei Li, Sen Liu, Kan Yang, and Xiaodong Lin · 2019
Cited alongside, same era.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Cited alongside, same era.
Privacy amplification via random check-ins
Borja Balle, Peter Kairouz, Brendan McMahan, Om Thakkar, and Abhradeep Guha Thakurta · 2020
Cited alongside, same era.
Secure single-server aggregation with (poly) logarithmic overhead
James Henry Bell, Kallista A Bonawitz, Adrià Gascón, Tancrède Lepoint, and Mariana Raykova · 2020
Cited alongside, same era.
The limitations of federated learning in sybil settings
Clement Fung, Chris JM Yoon, and Ivan Beschastnikh · 2020
Cited alongside, same era.
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Differentially private federated learning with shuffling and client self-sampling
Antonious M Girgis, Deepesh Data, and Suhas Diggavi · 2021
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Proof-of-learning: Definitions and practice
Hengrui Jia, Mohammad Yaghini, Christopher A Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, and Nicolas Papernot · 2021
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The distributed discrete gaussian mechanism for federated learning with secure aggregation
Peter Kairouz, Ziyu Liu, and Thomas Steinke · 2021
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Practical and private (deep) learning without sampling or shuffling
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu · 2021
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Ppfl: privacy-preserving federated learning with trusted execution environments
Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, and Nicolas Kourtellis · 2021
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Eluding secure aggregation in federated learning via model inconsistency
Dario Pasquini, Danilo Francati, and Giuseppe Ateniese · 2021
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Biscotti: A blockchain system for private and secure federated learning
Muhammad Shayan, Clement Fung, Chris J. M. Yoon, and Ivan Beschastnikh · 2021
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Manipulating SGD with Data Ordering Attacks
Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, and Ross Anderson · 2021
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Haibo Yang, Xin Zhang, Prashant Khanduri, and Jia Liu · 2021
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See through gradients: Image batch recovery via gradinversion
Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M Alvarez, Jan Kautz, and Pavlo Molchanov · 2021
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The fundamental price of secure aggregation in differentially private federated learning
Wei-Ning Chen, Christopher A Choquette-Choo, Peter Kairouz, and Ananda Theertha Suresh · 2022
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Federated learning with formal differential privacy guarantees
Brendan McMahan and Abhradeep Thakurta · 2022
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On the privacy of decentralized machine learning
Dario Pasquini, Mathilde Raynal, and Carmela Troncoso · 2022
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Bounding membership inference, 2022
Anvith Thudi, Ilia Shumailov, Franziska Boenisch, and Nicolas Papernot · 2022
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Android 12 adds ai and machine learning with private compute core but keeps your data secure
Jack Wallen · 2022
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Fishing for user data in large-batch federated learning via gradient magnification
Yuxin Wen, Jonas A. Geiping, Liam Fowl, Micah Goldblum, and Tom Goldstein · 2022
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