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1911.11607
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Deep Learning with Gaussian Differential Privacy
Harvard data science review (HDSR), 2019
26 November 2019
Zhiqi Bu
Jinshuo Dong
Qi Long
Weijie J. Su
FedML
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Papers citing
"Deep Learning with Gaussian Differential Privacy"
50 / 119 papers shown
ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push
Xiaoming Wu
Teng Liu
Xin Wang
Ming Yang
Jiguo Yu
149
1
0
23 Oct 2025
Gaussian Certified Unlearning in High Dimensions: A Hypothesis Testing Approach
Aaradhya Pandey
Arnab Auddy
Haolin Zou
A. Maleki
Sanjeev Kulkarni
MU
198
2
0
15 Oct 2025
Prismo: A Decision Support System for Privacy-Preserving ML Framework Selection
Nges Brian Njungle
Eric Jahns
Luigi Mastromauro
Edwin P. Kayang
Milan Stojkov
Michel Kinsy
169
0
0
11 Oct 2025
SoftAdaClip: A Smooth Clipping Strategy for Fair and Private Model Training
Dorsa Soleymani
Ali Dadsetan
Frank Rudzicz
239
1
0
01 Oct 2025
Statistical Inference for Differentially Private Stochastic Gradient Descent
Xintao Xia
Linjun Zhang
Zhanrui Cai
255
0
0
28 Jul 2025
Dyn-D
2
^2
2
P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee
International Joint Conference on Artificial Intelligence (IJCAI), 2025
Zehan Zhu
Yan Huang
Xin Wang
Shouling Ji
Jinming Xu
329
0
0
10 May 2025
Federated Learning with Differential Privacy: An Utility-Enhanced Approach
Kanishka Ranaweera
Dinh C. Nguyen
P. Pathirana
David B. Smith
Ming Ding
Thierry Rakotoarivelo
A. Seneviratne
FedML
269
5
0
27 Mar 2025
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
Juan Felipe Gomez
B. Kulynych
G. Kaissis
Jamie Hayes
Jamie Hayes
Borja Balle
Antti Honkela
454
0
0
13 Mar 2025
Towards hyperparameter-free optimization with differential privacy
International Conference on Learning Representations (ICLR), 2025
Zhiqi Bu
Ruixuan Liu
331
7
0
02 Mar 2025
Gradients Stand-in for Defending Deep Leakage in Federated Learning
H. Yi
H. Ren
C. Hu
Y. Li
J. Deng
Xin Xie
FedML
243
1
0
11 Oct 2024
The 2020 United States Decennial Census Is More Private Than You (Might) Think
Buxin Su
Weijie J. Su
Chendi Wang
543
5
0
11 Oct 2024
A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem
Annual Review of Statistics and Its Application (ARSIA), 2024
Weijie J. Su
350
8
0
14 Sep 2024
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
Huiwen Wu
Xiaohan Li
Deyi Zhang
Xiaohan Li
Yan Han
Puning Zhao
FedML
394
3
0
22 May 2024
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning
International Conference on Machine Learning (ICML), 2024
Chendi Wang
Yuqing Zhu
Weijie J. Su
Yu Wang
AAML
278
8
0
14 May 2024
PATE-TripleGAN: Privacy-Preserving Image Synthesis with Gaussian Differential Privacy
Zepeng Jiang
Weiwei Ni
Yifan Zhang
PICV
387
1
0
19 Apr 2024
Advances in Differential Privacy and Differentially Private Machine Learning
Saswat Das
Subhankar Mishra
295
9
0
06 Apr 2024
Shifted Interpolation for Differential Privacy
Jinho Bok
Weijie Su
Jason M. Altschuler
433
12
0
01 Mar 2024
Pre-training Differentially Private Models with Limited Public Data
Zhiqi Bu
Xinwei Zhang
Mingyi Hong
Sheng Zha
George Karypis
323
6
0
28 Feb 2024
LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification
Yiping Song
Juhua Zhang
Zhiliang Tian
Yuxin Yang
Shiyu Huang
Dongsheng Li
202
17
0
26 Feb 2024
Differentially Private Fair Binary Classifications
Hrad Ghoukasian
S. Asoodeh
FaML
281
6
0
23 Feb 2024
Decentralized Federated Learning: A Survey on Security and Privacy
IEEE Transactions on Big Data (IEEE Trans. Big Data), 2024
Ehsan Hallaji
R. Razavi-Far
R. Razavi-Far
Boyu Wang
Qiang Yang
FedML
327
111
0
25 Jan 2024
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
Hanlin Gu
Xinyuan Zhao
Gongxi Zhu
Yuxing Han
Weijing Chen
Lixin Fan
Qiang Yang
FedML
265
2
0
27 Dec 2023
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)
Xingyuan Zhao
Fang Liu
197
0
0
20 Dec 2023
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance
Haichao Sha
Ruixuan Liu
Yi-xiao Liu
Hong Chen
330
2
0
06 Dec 2023
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective
Xianghua Xie
Chen Hu
Hanchi Ren
Jingjing Deng
FedML
AAML
302
40
0
27 Nov 2023
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release
Proceedings of the VLDB Endowment (PVLDB), 2023
Jie Fu
Qingqing Ye
Haibo Hu
Zhili Chen
Lulu Wang
Kuncan Wang
Xun Ran
391
28
0
23 Nov 2023
Zero redundancy distributed learning with differential privacy
Zhiqi Bu
Justin Chiu
Ruixuan Liu
Sheng Zha
George Karypis
303
9
0
20 Nov 2023
Verification of Neural Networks Local Differential Classification Privacy
International Conference on Verification, Model Checking and Abstract Interpretation (VMCAI), 2023
Roie Reshef
Anan Kabaha
Olga Seleznova
Dana Drachsler-Cohen
AAML
226
3
0
31 Oct 2023
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via
f
f
f
-Differential Privacy
Neural Information Processing Systems (NeurIPS), 2023
Chendi Wang
Buxin Su
Jiayuan Ye
Reza Shokri
Weijie J. Su
FedML
408
18
0
30 Oct 2023
On the accuracy and efficiency of group-wise clipping in differentially private optimization
Zhiqi Bu
Ruixuan Liu
Yu Wang
Sheng Zha
George Karypis
VLM
238
5
0
30 Oct 2023
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
The Web Conference (WWW), 2023
Zheyuan Liu
Guangyao Dou
Yijun Tian
Chunhui Zhang
Eli Chien
Ziwei Zhu
MU
328
32
0
28 Oct 2023
Coupling public and private gradient provably helps optimization
Ruixuan Liu
Zhiqi Bu
Yu Wang
Sheng Zha
George Karypis
326
3
0
02 Oct 2023
DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning
S. Giddens
Fan Liu
227
1
0
19 Sep 2023
DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass
Conference on Computer and Communications Security (CCS), 2023
Minxin Du
Xiang Yue
Sherman S. M. Chow
Tianhao Wang
Chenyu Huang
Huan Sun
SILM
451
105
0
13 Sep 2023
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Jiawei Shao
Zijian Li
Wenqiang Sun
Tailin Zhou
Yuchang Sun
Lumin Liu
Zehong Lin
Yuyi Mao
Jun Zhang
FedML
336
46
0
20 Jul 2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy
Georgios Kaissis
Jamie Hayes
Alexander Ziller
Daniel Rueckert
AAML
260
19
0
08 Jul 2023
Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning
Tamas Madl
Weijie Xu
Olivia Choudhury
Matthew Howard
148
8
0
04 Jul 2023
DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum
Xiaolan Gu
Ming Li
Lishuang Xiong
FedML
350
11
0
22 Jun 2023
Differentially Private Wireless Federated Learning Using Orthogonal Sequences
Xizixiang Wei
Tianhao Wang
Ruiquan Huang
Cong Shen
Jing Yang
H. Vincent Poor
286
1
0
14 Jun 2023
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
Alyssa Huang
Peihan Liu
Ryumei Nakada
Linjun Zhang
Wanrong Zhang
VLM
441
8
0
13 Jun 2023
Personalized DP-SGD using Sampling Mechanisms
Geon Heo
Junseok Seo
Steven Euijong Whang
243
4
0
24 May 2023
Gradient Leakage Defense with Key-Lock Module for Federated Learning
Hanchi Ren
Jingjing Deng
Xianghua Xie
FedML
512
3
0
06 May 2023
A Randomized Approach for Tight Privacy Accounting
Neural Information Processing Systems (NeurIPS), 2023
Jiachen T. Wang
Saeed Mahloujifar
Tong Wu
R. Jia
Prateek Mittal
385
12
0
17 Apr 2023
Fairness-aware Differentially Private Collaborative Filtering
The Web Conference (WWW), 2023
Zhenhuan Yang
Yingqiang Ge
Congzhe Su
Dingxian Wang
Xiaoting Zhao
Yiming Ying
FedML
277
6
0
16 Mar 2023
Breaking the Communication-Privacy-Accuracy Tradeoff with
f
f
f
-Differential Privacy
Neural Information Processing Systems (NeurIPS), 2023
Richeng Jin
Z. Su
C. Zhong
Zhaoyang Zhang
Tony Q.S. Quek
H. Dai
FedML
351
5
0
19 Feb 2023
Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records
Bingyue Su
Yu Wang
Daniele E. Schiavazzi
Fang Liu
155
2
0
11 Feb 2023
z
z
z
-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning
AAAI Conference on Artificial Intelligence (AAAI), 2023
Zhiwei Tang
Yanmeng Wang
Tsung-Hui Chang
FedML
322
24
0
06 Feb 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions
Findings (Findings), 2023
Lijie Hu
Ivan Habernal
Lei Shen
Haiyan Zhao
AAML
309
25
0
22 Jan 2023
LDL: A Defense for Label-Based Membership Inference Attacks
ACM Asia Conference on Computer and Communications Security (AsiaCCS), 2022
Arezoo Rajabi
D. Sahabandu
Luyao Niu
Bhaskar Ramasubramanian
Radha Poovendran
AAML
172
4
0
03 Dec 2022
Differentially Private Optimizers Can Learn Adversarially Robust Models
Yuan Zhang
Zhiqi Bu
343
5
0
16 Nov 2022
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