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Data Heterogeneity Differential Privacy: From Theory to Algorithm

Data Heterogeneity Differential Privacy: From Theory to Algorithm

Yilin Kang
Jian Li
Yong Liu
Weiping Wang
ArXivPDFHTML

Papers citing "Data Heterogeneity Differential Privacy: From Theory to Algorithm"

6 / 6 papers shown
Title
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints
Shaojie Li
Yong Liu
53
13
0
19 Jul 2021
Differentially Private Bayesian Linear Regression
Differentially Private Bayesian Linear Regression
G. Bernstein
Daniel Sheldon
55
58
0
29 Oct 2019
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM
Bao Wang
Quanquan Gu
M. Boedihardjo
Farzin Barekat
Stanley J. Osher
59
25
0
28 Jun 2019
Understanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence Functions
Pang Wei Koh
Percy Liang
TDI
98
2,854
0
14 Mar 2017
Membership Inference Attacks against Machine Learning Models
Membership Inference Attacks against Machine Learning Models
Reza Shokri
M. Stronati
Congzheng Song
Vitaly Shmatikov
SLR
MIALM
MIACV
194
4,075
0
18 Oct 2016
A Proximal Stochastic Gradient Method with Progressive Variance Reduction
A Proximal Stochastic Gradient Method with Progressive Variance Reduction
Lin Xiao
Tong Zhang
ODL
135
738
0
19 Mar 2014

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