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Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning

25 February 2021

Papers citing "Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning"

33 / 33 papers shown

Title
Differentially Private Kernel Density Estimation Erzhi Liu Jerry Yao-Chieh Hu Alex Reneau Zhao Song Han Liu 135 3 0 03 Sep 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization Xinyu Tang Ashwinee Panda Milad Nasr Saeed Mahloujifar Prateek Mittal 199 26 0 09 Jan 2024
Fast Dimension Independent Private AdaGrad on Publicly Estimated Subspaces Peter Kairouz Mónica Ribero Keith Rush Abhradeep Thakurta 138 14 0 14 Aug 2020
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification Yingxue Zhou Zhiwei Steven Wu A. Banerjee 70 110 0 07 Jul 2020
Low-rank Gradient Approximation For Memory-Efficient On-device Training of Deep Neural Network Mary Gooneratne K. Sim P. Zadrazil Andreas Kabel F. Beaufays Giovanni Motta 130 24 0 24 Jan 2020
Assessing differentially private deep learning with Membership Inference Daniel Bernau Philip-William Grassal J. Robl Florian Kerschbaum MIACV FedML 71 23 0 24 Dec 2019
Momentum Contrast for Unsupervised Visual Representation Learning Kaiming He Haoqi Fan Yuxin Wu Saining Xie Ross B. Girshick SSL 216 12,136 0 13 Nov 2019
Limits of Private Learning with Access to Public Data N. Alon Raef Bassily Shay Moran 54 49 0 25 Oct 2019
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference Alexandre Sablayrolles Matthijs Douze Yann Ollivier Cordelia Schmid Hervé Jégou MIACV 81 369 0 29 Aug 2019
Rényi Differential Privacy of the Sampled Gaussian Mechanism Ilya Mironov Kunal Talwar Li Zhang 103 287 0 28 Aug 2019
Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization Xinyan Li Qilong Gu Yingxue Zhou Tiancong Chen A. Banerjee ODL 80 52 0 24 Jul 2019
Deep Leakage from Gradients Ligeng Zhu Zhijian Liu Song Han FedML 105 2,229 0 21 Jun 2019
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization Thijs Vogels Sai Praneeth Karimireddy Martin Jaggi 99 322 0 31 May 2019
Data Poisoning against Differentially-Private Learners: Attacks and Defenses Yuzhe Ma Xiaojin Zhu Justin Hsu SILM 56 158 0 23 Mar 2019
Gradient Descent Happens in a Tiny Subspace Guy Gur-Ari Daniel A. Roberts Ethan Dyer 103 234 0 12 Dec 2018
Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget Jaewoo Lee Daniel Kifer 48 158 0 28 Aug 2018
Subsampled Rényi Differential Privacy and Analytical Moments Accountant Yu Wang Borja Balle S. Kasiviswanathan 90 401 0 31 Jul 2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 87 393 0 04 Jul 2018
Group Normalization Yuxin Wu Kaiming He 249 3,676 0 22 Mar 2018
Scalable Private Learning with PATE Nicolas Papernot Shuang Song Ilya Mironov A. Raghunathan Kunal Talwar Ulfar Erlingsson 125 618 0 24 Feb 2018
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks Nicholas Carlini Chang-rui Liu Ulfar Erlingsson Jernej Kos Basel Alomair 165 1,150 0 22 Feb 2018
Differentially Private Empirical Risk Minimization Revisited: Faster and More General Di Wang Minwei Ye Jinhui Xu 130 273 0 14 Feb 2018
Differentially Private Empirical Risk Minimization with Input Perturbation Kazuto Fukuchi Quang Khai Tran Jun Sakuma 68 36 0 20 Oct 2017
Renyi Differential Privacy Ilya Mironov 93 1,268 0 24 Feb 2017
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning Briland Hitaj G. Ateniese Fernando Perez-Cruz FedML 141 1,413 0 24 Feb 2017
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 283 4,168 0 18 Oct 2016
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data Nicolas Papernot Martín Abadi Ulfar Erlingsson Ian Goodfellow Kunal Talwar 100 1,021 0 18 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 223 6,172 0 01 Jul 2016
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds Mark Bun Thomas Steinke 97 840 0 06 May 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.3K 194,641 0 10 Dec 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 469 43,357 0 11 Feb 2015
An Introduction to Matrix Concentration Inequalities J. Tropp 178 1,155 0 07 Jan 2015
Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds Raef Bassily Adam D. Smith Abhradeep Thakurta FedML 153 371 0 27 May 2014