Fetching the paper…
Reading the bibliography…
The privacy leakage of the model about the training data can be bounded in the differential privacy mechanism.
The non-singularity of generalized sample covariance matrices
Morris L Eaton and Michael D Perlman · 1973
Earlier work this paper cites.
An elementary proof of a theorem of johnson and lindenstrauss
Sanjoy Dasgupta and Anupam Gupta · 2003
Earlier work this paper cites.
Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2006
Earlier work this paper cites.
Statistical properties of kernel principal component analysis
Gilles Blanchard, Olivier Bousquet, and Laurent Zwald · 2007
Earlier work this paper cites.
Aspects of multivariate statistical theory , volume 197
Robb J Muirhead · 2009
Earlier work this paper cites.
Iterative methods for computing eigenvalues and eigenvectors
Maysum Panju · 2011
Earlier work this paper cites.
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 with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Earlier work this paper cites.
Differentially private feature selection via stability arguments, and the robustness of the lasso
Abhradeep Guha Thakurta and Adam Smith · 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.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Earlier work this paper cites.
Nearly optimal private lasso
Kunal Talwar, Abhradeep Guha Thakurta, and Li Zhang · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 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.
A methodology for formalizing model-inversion attacks
Xi Wu, Matthew Fredrikson, Somesh Jha, and Jeffrey F Naughton · 2016
Earlier work this paper cites.
Differentially private empirical risk minimization with input perturbation
Kazuto Fukuchi, Quang Khai Tran, and Jun Sakuma · 2017
Earlier work this paper cites.
Deep models under the gan: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Pérez-Cruz · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 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.
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
Data poisoning against differentially-private learners: attacks and defenses
Yuzhe Ma, Xiaojin Zhu, and Justin Hsu · 2019
Later among the works it cites.
Rényi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang · 2019
Later among the works it cites.
White-box vs black-box: Bayes optimal strategies for membership inference
Alexandre Sablayrolles, Matthijs Douze, Yann Ollivier, Cordelia Schmid, and Hervé Jégou · 2019
Later among the works it cites.
Powersgd: Practical low-rank gradient compression for distributed optimization
Thijs Vogels, Sai Praneeth Karimireddy, and Martin Jaggi · 2019
Later among the works it cites.
On sparse linear regression in the local differential privacy model
Di Wang and Jinhui Xu · 2019
Later among the works it cites.
Differentially private iterative gradient hard thresholding for sparse learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
Cited alongside, same era.
Gradient descent happens in a tiny subspace
Guy Gur-Ari, Daniel A Roberts, and Ethan Dyer · 2018
Cited alongside, same era.
Distributed learning without distress: Privacy-preserving empirical risk minimization
Bargav Jayaraman, Lingxiao Wang, David Evans, and Quanquan Gu · 2018
Cited alongside, same era.
Concentrated differentially private gradient descent with adaptive per-iteration privacy budget
Jaewoo Lee and Daniel Kifer · 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.
Membership inference attack against differentially private deep learning model
Md Atiqur Rahman, Tanzila Rahman, Robert Laganiere, Noman Mohammed, and Yang Wang · 2018
Cited alongside, same era.
Lingxiao Wang and Quanquan Gu · 2019
Later among the works it cites.
Subsampled rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
Later among the works it cites.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Later among the works it cites.
Low-rank gradient approximation for memory-efficient on-device training of deep neural network
Mary Gooneratne, Khe Chai Sim, Petr Zadrazil, Andreas Kabel, Françoise Beaufays, and Giovanni Motta · 2020
Later among the works it cites.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Later among the works it cites.
Dimension independence in unconstrained private erm via adaptive preconditioning
Peter Kairouz, Mónica Ribero, Keith Rush, and Abhradeep Thakurta · 2020
Later among the works it cites.
Hessian based analysis of sgd for deep nets: Dynamics and generalization
Xinyan Li, Qilong Gu, Yingxue Zhou, Tiancong Chen, and Arindam Banerjee · 2020
Later among the works it cites.
Scalable differential privacy with certified robustness in adversarial learning
NhatHai Phan, My T Thai, Han Hu, Ruoming Jin, Tong Sun, and Dejing Dou · 2020
Later among the works it cites.
Differentially private learning needs better features (or much more data)
Florian Tramèr and Dan Boneh · 2020
Later among the works it cites.
Differentially private learning with small public data
Jun Wang and Zhi-Hua Zhou · 2020
Later among the works it cites.
Gradient perturbation is underrated for differentially private convex optimization
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu · 2020
Later among the works it cites.
Bypassing the ambient dimension: Private sgd with gradient subspace identification
Yingxue Zhou, Zhiwei Steven Wu, and Arindam Banerjee · 2020
Later among the works it cites.
Poission subsampled rényi differential privacy
Yuqing Zhu and Yu-Xiang Wang · 2020
Later among the works it cites.
How does data augmentation affect privacy in machine learning?
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu · 2021
Closest in time.