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1908.10530
Cited By
Rényi Differential Privacy of the Sampled Gaussian Mechanism
28 August 2019
Ilya Mironov
Kunal Talwar
Li Zhang
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ArXiv (abs)
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Papers citing
"Rényi Differential Privacy of the Sampled Gaussian Mechanism"
50 / 182 papers shown
Title
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training
Tom Sander
Maxime Sylvestre
Alain Durmus
67
1
0
13 Feb 2024
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation
Ossi Raisa
Hibiki Ito
Antti Honkela
77
6
0
06 Feb 2024
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
Cong Fang
Adam Dziedzic
Lin Zhang
Laura Oliva
A. Verma
Fahad Razak
Nicolas Papernot
Bo Wang
OOD
45
14
0
31 Jan 2024
Cross-silo Federated Learning with Record-level Personalized Differential Privacy
Junxu Liu
Jian Lou
Li Xiong
Jinfei Liu
Xiaofeng Meng
92
7
0
29 Jan 2024
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
Rachel Redberg
Antti Koskela
Yu-Xiang Wang
148
6
0
31 Dec 2023
DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)
Qiaoyue Tang
Frederick Shpilevskiy
Mathias Lécuyer
73
17
0
21 Dec 2023
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
Xinwei Zhang
Zhiqi Bu
Zhiwei Steven Wu
Mingyi Hong
74
7
0
24 Nov 2023
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
70
17
0
23 Nov 2023
Preserving Node-level Privacy in Graph Neural Networks
Zihang Xiang
Tianhao Wang
Di Wang
74
12
0
12 Nov 2023
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via
f
f
f
-Differential Privacy
Chendi Wang
Buxin Su
Jiayuan Ye
Reza Shokri
Weijie J. Su
FedML
80
11
0
30 Oct 2023
DP-SGD with weight clipping
Antoine Barczewski
Jan Ramon
94
1
0
27 Oct 2023
Privacy Amplification for Matrix Mechanisms
Christopher A. Choquette-Choo
Arun Ganesh
Thomas Steinke
Abhradeep Thakurta
84
11
0
24 Oct 2023
PrivImage: Differentially Private Synthetic Image Generation using Diffusion Models with Semantic-Aware Pretraining
Kecen Li
Chen Gong
Zhixiang Li
Yuzhong Zhao
Xinwen Hou
Tianhao Wang
99
10
0
19 Oct 2023
Differentially Private Data Generation with Missing Data
Shubhankar Mohapatra
Jianqiao Zong
F. Kerschbaum
Xi He
SyDa
70
1
0
17 Oct 2023
A Unified View of Differentially Private Deep Generative Modeling
Dingfan Chen
Raouf Kerkouche
Mario Fritz
SyDa
85
5
0
27 Sep 2023
PA-iMFL: Communication-Efficient Privacy Amplification Method against Data Reconstruction Attack in Improved Multi-Layer Federated Learning
Jianhua Wang
Xiaolin Chang
Jelena Mivsić
Vojislav B. Mivsić
Zhi Chen
Junchao Fan
63
3
0
25 Sep 2023
Communication Efficient Private Federated Learning Using Dithering
Burak Hasircioglu
Deniz Gunduz
FedML
119
7
0
14 Sep 2023
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning
Zebang Shen
Jiayuan Ye
Anmin Kang
Hamed Hassani
Reza Shokri
FedML
97
18
0
11 Sep 2023
Geometry of Sensitivity: Twice Sampling and Hybrid Clipping in Differential Privacy with Optimal Gaussian Noise and Application to Deep Learning
Hanshen Xiao
Jun Wan
Srini Devadas
48
8
0
06 Sep 2023
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
Fumiyuki Kato
Li Xiong
Shun Takagi
Yang Cao
Masatoshi Yoshikawa
FedML
92
4
0
23 Aug 2023
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
Xinpeng Ling
Jie Fu
Kuncan Wang
Haitao Liu
Zhili Chen
FedML
109
2
0
21 Aug 2023
Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models
Phillip Rust
Anders Søgaard
66
3
0
17 Aug 2023
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks
Shijie Liu
Andrew C. Cullen
Paul Montague
S. Erfani
Benjamin I. P. Rubinstein
AAML
66
6
0
15 Aug 2023
Large-Scale Public Data Improves Differentially Private Image Generation Quality
Ruihan Wu
Chuan Guo
Kamalika Chaudhuri
96
2
0
04 Aug 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis
Kunal Talwar
Shan Wang
Audra McMillan
Vojta Jina
Vitaly Feldman
...
Congzheng Song
Karl Tarbe
Sebastian Vogt
L. Winstrom
Shundong Zhou
FedML
161
14
0
27 Jul 2023
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering
Ce Feng
Nuo Xu
Wujie Wen
Parv Venkitasubramaniam
Caiwen Ding
63
4
0
25 Jul 2023
Population Expansion for Training Language Models with Private Federated Learning
Tatsuki Koga
Congzheng Song
Martin Pelikan
Mona Chitnis
FedML
50
1
0
14 Jul 2023
Personalized Privacy Amplification via Importance Sampling
Dominik Fay
Sebastian Mair
Jens Sjölund
136
0
0
05 Jul 2023
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
Anvith Thudi
Hengrui Jia
Casey Meehan
Ilia Shumailov
Nicolas Papernot
117
7
0
01 Jul 2023
ViP: A Differentially Private Foundation Model for Computer Vision
Yaodong Yu
Maziar Sanjabi
Yi Ma
Kamalika Chaudhuri
Chuan Guo
65
13
0
15 Jun 2023
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
Jiapeng Wu
Atiyeh Ashari Ghomi
David Glukhov
Jesse C. Cresswell
Franziska Boenisch
Nicolas Papernot
AAML
87
2
0
14 Jun 2023
Differentially Private Wireless Federated Learning Using Orthogonal Sequences
Xizixiang Wei
Tianhao Wang
Ruiquan Huang
Cong Shen
Jing Yang
H. Vincent Poor
104
1
0
14 Jun 2023
(Amplified) Banded Matrix Factorization: A unified approach to private training
Christopher A. Choquette-Choo
Arun Ganesh
Ryan McKenna
H. B. McMahan
Keith Rush
Abhradeep Thakurta
Zheng Xu
FedML
111
41
0
13 Jun 2023
Privately generating tabular data using language models
Alexandre Sablayrolles
Yue Wang
Brian Karrer
LMTD
80
5
0
07 Jun 2023
DP-SGD Without Clipping: The Lipschitz Neural Network Way
Louis Bethune
Thomas Massena
Thibaut Boissin
Yannick Prudent
Corentin Friedrich
Franck Mamalet
A. Bellet
M. Serrurier
David Vigouroux
98
9
0
25 May 2023
Faster Differentially Private Convex Optimization via Second-Order Methods
Arun Ganesh
Mahdi Haghifam
Thomas Steinke
Abhradeep Thakurta
40
13
0
22 May 2023
Privacy Auditing with One (1) Training Run
Thomas Steinke
Milad Nasr
Matthew Jagielski
118
87
0
15 May 2023
DPMLBench: Holistic Evaluation of Differentially Private Machine Learning
Chengkun Wei
Ming-Hui Zhao
Zhikun Zhang
Min Chen
Wenlong Meng
Bodong Liu
Yuan-shuo Fan
Wenzhi Chen
96
11
0
10 May 2023
Practical Differentially Private and Byzantine-resilient Federated Learning
Zihang Xiang
Tianhao Wang
Wanyu Lin
Di Wang
FedML
73
23
0
15 Apr 2023
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy
Kang Wei
Jun Li
Chuan Ma
Ming Ding
Feng Shu
Haitao Zhao
Wen Chen
Hongbo Zhu
FedML
126
6
0
09 Apr 2023
Have it your way: Individualized Privacy Assignment for DP-SGD
Franziska Boenisch
Christopher Muhl
Adam Dziedzic
Roy Rinberg
Nicolas Papernot
85
18
0
29 Mar 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Natalia Ponomareva
Hussein Hazimeh
Alexey Kurakin
Zheng Xu
Carson E. Denison
H. B. McMahan
Sergei Vassilvitskii
Steve Chien
Abhradeep Thakurta
156
183
0
01 Mar 2023
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
Edwige Cyffers
A. Bellet
D. Basu
FedML
101
5
0
24 Feb 2023
Private GANs, Revisited
Alex Bie
Gautam Kamath
Guojun Zhang
104
16
0
06 Feb 2023
z
z
z
-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning
Zhiwei Tang
Yanmeng Wang
Tsung-Hui Chang
FedML
70
14
0
06 Feb 2023
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations
Hui-Po Wang
Dingfan Chen
Raouf Kerkouche
Mario Fritz
FedML
DD
90
4
0
02 Feb 2023
Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models
Florian A. Hölzl
Daniel Rueckert
Georgios Kaissis
95
4
0
30 Jan 2023
Practical Differentially Private Hyperparameter Tuning with Subsampling
A. Koskela
Tejas D. Kulkarni
114
17
0
27 Jan 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions
Lijie Hu
Ivan Habernal
Lei Shen
Di Wang
AAML
94
19
0
22 Jan 2023
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy
Ergute Bao
Yizheng Zhu
X. Xiao
Yifan Yang
Beng Chin Ooi
B. Tan
Khin Mi Mi Aung
FedML
82
19
0
08 Dec 2022
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