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Gradient perturbation, widely used for differentially private optimization, injects noise at every iterative update to guarantee differential privacy.
Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
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Private convex empirical risk minimization and high-dimensional regression
Daniel Kifer, Adam Smith, and Abhradeep Thakurta · 2012
Earlier work this paper cites.
Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Ohad Shamir and Tong Zhang · 2013
Earlier work this paper cites.
Differentially private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
Earlier work this paper cites.
Communication-efficient distributed optimization using an approximate newton-type method
Ohad Shamir, Nati Srebro, and Tong Zhang · 2014
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Efficient per-example gradient computations
Ian Goodfellow · 2015
Earlier work this paper cites.
Federated optimization: Distributed optimization beyond the datacenter
Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage · 2015
Cited alongside, same era.
Nearly optimal private lasso
Kunal Talwar, Abhradeep Guha Thakurta, and Li Zhang · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
The challenge of scientific reproducibility and privacy protection for statistical agencies
John M. Abowd · 2016
Cited alongside, same era.
Deep models under the gan: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Pérez-Cruz · 2017
Cited alongside, same era.
Differentially private convex optimization benchmark, 2017
Bolt-on differential privacy for scalable stochastic gradient descent-based analytics
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, and Jeffrey Naughton · 2017
Later among the works it cites.
Efficient private erm for smooth objectives
Jiaqi Zhang, Kai Zheng, Wenlong Mou, and Liwei Wang · 2017
Later among the works it cites.
cpsgd: Communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, and Brendan McMahan · 2018
Later among the works it cites.
Privacy amplification by iteration
Vitaly Feldman, Ilya Mironov, Kunal Talwar, and Abhradeep Thakurta · 2018
Later among the works it cites.
Distributed learning without distress: Privacy-preserving empirical risk minimization
Bargav Jayaraman, Lingxiao Wang, David Evans, and Quanquan Gu · 2018
Later among the works it cites.
Concentrated differentially private gradient descent with adaptive per-iteration privacy budget
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Roger Iyengar, Joseph P. Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, and Lun Wang · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Differentially private empirical risk minimization revisited: Faster and more general
Di Wang, Minwei Ye, and Jinhui Xu · 2017
Cited alongside, same era.
Jaewoo Lee and Daniel Kifer · 2018
Later among the works it cites.
Membership inference attack against differentially private deep learning model
Md Atiqur Rahman, Tanzila Rahman, Robert Laganiere, Noman Mohammed, and Yang Wang · 2018
Later among the works it cites.
Towards practical differentially private convex optimization
Roger Iyengar, Joseph P. Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, and Lun Wang · 2019
Closest in time.
Differentially private iterative gradient hard thresholding for sparse learning
Lingxiao Wang and Quanquan Gu · 2019
Closest in time.