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Most existing approaches of differentially private (DP) machine learning focus on private training.
Similarity search in high dimensions via hashing
Aristides Gionis, Piotr Indyk, Rajeev Motwani, et al · 1999
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Earlier work this paper cites.
The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’
Alessandro Mantelero · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Dbpedia - a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick van Kleef, S. Auer, and Christian Bizer · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Psi ( ψ \psi ): a private data sharing interface
Marco Gaboardi, James Honaker, Gary King, Kobbi Nissim, Jonathan Ullman, Salil Vadhan, and Jack Murtagh · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Privacy odometers and filters: Pay-as-you-go composition
Ryan M Rogers, Aaron Roth, Jonathan Ullman, and Salil Vadhan · 2016
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Model-agnostic private learning
Raef Bassily, Om Thakkar, and Abhradeep Guha Thakurta · 2018
Cited alongside, same era.
Privacy-preserving prediction
Cynthia Dwork and Vitaly Feldman · 2018
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Later among the works it cites.
Pac learning with stable and private predictions
Yuval Dagan and Vitaly Feldman · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 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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The trade-offs of private prediction
Laurens van der Maaten and Awni Hannun · 2020
Later among the works it cites.
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Cited alongside, same era.
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
Cited alongside, same era.
Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
Cited alongside, same era.
Hypothesis testing interpretations and renyi differential privacy
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, and Tetsuya Sato · 2020
Cited alongside, same era.
Private-knn: Practical differential privacy for computer vision
Yuqing Zhu, Xiang Yu, Manmohan Chandraker, and Yu-Xiang Wang · 2020
Later among the works it cites.
Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
Later among the works it cites.
Individual privacy accounting via a renyi filter
Vitaly Feldman and Tijana Zrnic · 2021
Later among the works it cites.
Differentially private learning needs better features (or much more data)
Florian Tramèr and Dan Boneh · 2021
Later among the works it cites.
Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
Later among the works it cites.
Compressing sentence representation for semantic retrieval via homomorphic projective distillation
Xuandong Zhao, Zhiguo Yu, Ming Wu, and Lei Li · 2022
Later among the works it cites.