Toward Training at ImageNet Scale with Differential Privacy

28 January 2022

Papers citing "Toward Training at ImageNet Scale with Differential Privacy"

32 / 82 papers shown

Title
Differentially Private Learning with Per-Sample Adaptive Clipping Tianyu Xia Shuheng Shen Su Yao Xinyi Fu Ke Xu Xiaolong Xu Xingbo Fu 30 16 0 01 Dec 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 52 7 0 24 Nov 2022
Private Ad Modeling with DP-SGD Carson E. Denison Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Krishnagiri Narra Amer Sinha A. Varadarajan Chiyuan Zhang 32 14 0 21 Nov 2022
Learning to Generate Image Embeddings with User-level Differential Privacy Zheng Xu Maxwell D. Collins Yuxiao Wang Liviu Panait Sewoong Oh S. Augenstein Ting Liu Florian Schroff H. B. McMahan FedML 30 29 0 20 Nov 2022
Differentially Private Optimizers Can Learn Adversarially Robust Models Yuan Zhang Zhiqi Bu 16 3 0 16 Nov 2022
Differentially Private Diffusion Models Tim Dockhorn Tianshi Cao Arash Vahdat Karsten Kreis DiffM 32 91 0 18 Oct 2022
Differentially Private Online-to-Batch for Smooth Losses Qinzi Zhang Hoang Tran Ashok Cutkosky FedML 46 4 0 12 Oct 2022
Differentially Private Deep Learning with ModelMix Hanshen Xiao Jun Wan S. Devadas 21 3 0 07 Oct 2022
TAN Without a Burn: Scaling Laws of DP-SGD Tom Sander Pierre Stock Alexandre Sablayrolles FedML 26 42 0 07 Oct 2022
Federated Boosted Decision Trees with Differential Privacy Samuel Maddock Graham Cormode Tianhao Wang Carsten Maple S. Jha FedML 21 29 0 06 Oct 2022
Fine-Tuning with Differential Privacy Necessitates an Additional Hyperparameter Search Yannis Cattan Christopher A. Choquette-Choo Nicolas Papernot Abhradeep Thakurta 26 20 0 05 Oct 2022
Differentially Private Optimization on Large Model at Small Cost Zhiqi Bu Yu-Xiang Wang Sheng Zha George Karypis 35 52 0 30 Sep 2022
Differentially Private Bias-Term Fine-tuning of Foundation Models Zhiqi Bu Yu-Xiang Wang Sheng Zha George Karypis 28 46 0 30 Sep 2022
DiVa: An Accelerator for Differentially Private Machine Learning Beom-Joo Park Ranggi Hwang Dongho Yoon Yoonhyuk Choi Minsoo Rhu 24 8 0 26 Aug 2022
Conflicting Interactions Among Protection Mechanisms for Machine Learning Models S. Szyller Nadarajah Asokan AAML 26 7 0 05 Jul 2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu-Xiang Wang Sheng Zha George Karypis 19 69 0 14 Jun 2022
Self-Supervised Pretraining for Differentially Private Learning Arash Asadian Evan Weidner Lei Jiang PICV 27 3 0 14 Jun 2022
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy Zhiqi Bu J. Mao Shiyun Xu 131 47 0 21 May 2022
Large Scale Transfer Learning for Differentially Private Image Classification Harsh Mehta Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 9 39 0 06 May 2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale Soham De Leonard Berrada Jamie Hayes Samuel L. Smith Borja Balle 35 217 0 28 Apr 2022
SoK: Differential Privacy on Graph-Structured Data Tamara T. Mueller Dmitrii Usynin Johannes C. Paetzold Daniel Rueckert Georgios Kaissis 19 14 0 17 Mar 2022
Differentially private training of residual networks with scale normalisation Helena Klause Alexander Ziller Daniel Rueckert Kerstin Hammernik Georgios Kaissis 6 32 0 01 Mar 2022
Differentially private stochastic expectation propagation (DP-SEP) Margarita Vinaroz Mijung Park 25 1 0 25 Nov 2021
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 134 346 0 13 Oct 2021
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 132 119 0 07 Oct 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 156 349 0 25 Sep 2021
On the Convergence and Calibration of Deep Learning with Differential Privacy Zhiqi Bu Hua Wang Zongyu Dai Qi Long 25 27 0 15 Jun 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 182 154 0 26 Feb 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 290 1,814 0 14 Dec 2020
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 144 178 0 28 Jul 2020
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 141 420 0 29 Nov 2018
Efficient Per-Example Gradient Computations Ian Goodfellow 186 74 0 07 Oct 2015