DPDR: Gradient Decomposition and Reconstruction for Differentially
Private Deep Learning

DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning

4 June 2024

Yixuan Liu

Yuhan Liu

Papers citing "DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning"

8 / 8 papers shown

Title
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release Jie Fu Qingqing Ye Haibo Hu Zhili Chen Lulu Wang Kuncan Wang Xun Ran 49 16 0 23 Nov 2023
Practical Differentially Private Hyperparameter Tuning with Subsampling A. Koskela Tejas D. Kulkarni 77 17 0 27 Jan 2023
Differentially Private Adaptive Optimization with Delayed Preconditioners Tian Li Manzil Zaheer Ziyu Liu Sashank J. Reddi H. B. McMahan Virginia Smith 74 11 0 01 Dec 2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu Wang Sheng Zha George Karypis 97 71 0 14 Jun 2022
Private Adaptive Gradient Methods for Convex Optimization Hilal Asi John C. Duchi Alireza Fallah O. Javidbakht Kunal Talwar 51 53 0 25 Jun 2021
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 174 179 0 28 Jul 2020
Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds Yingxue Zhou Xiangyi Chen Mingyi Hong Zhiwei Steven Wu A. Banerjee 53 25 0 24 Jun 2020
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 228 4,103 0 18 Oct 2016