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Opacus: User-Friendly Differential Privacy Library in PyTorch
25 September 2021
Ashkan Yousefpour
I. Shilov
Alexandre Sablayrolles
Davide Testuggine
Karthik Prasad
Mani Malek
John Nguyen
Sayan Gosh
Akash Bharadwaj
Jessica Zhao
Graham Cormode
Ilya Mironov
VLM
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Papers citing
"Opacus: User-Friendly Differential Privacy Library in PyTorch"
50 / 288 papers shown
SABLE: Secure And Byzantine robust LEarning
Antoine Choffrut
R. Guerraoui
Rafael Pinot
Renaud Sirdey
John Stephan
Martin Zuber
AAML
399
2
0
11 Sep 2023
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
Daniel Scheliga
Patrick Mäder
M. Seeland
FedML
AAML
331
8
0
08 Sep 2023
Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy
IEEE Conference on Communications and Network Security (CNS), 2023
Zikai Zhang
Rui Hu
193
15
0
07 Sep 2023
The Relative Gaussian Mechanism and its Application to Private Gradient Descent
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Aymeric Dieuleveut
Paul Mangold
A. Bellet
316
1
0
29 Aug 2023
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
Proceedings of the VLDB Endowment (PVLDB), 2023
Fumiyuki Kato
Li Xiong
Shun Takagi
Yang Cao
Masatoshi Yoshikawa
FedML
284
8
0
23 Aug 2023
A novel analysis of utility in privacy pipelines, using Kronecker products and quantitative information flow
Conference on Computer and Communications Security (CCS), 2023
Mário S. Alvim
Natasha Fernandes
Annabelle McIver
Carroll Morgan
Gabriel H. Nunes
207
6
0
22 Aug 2023
Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models
International Conference on Machine Learning (ICML), 2023
Phillip Rust
Anders Søgaard
175
6
0
17 Aug 2023
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks
AAAI Conference on Artificial Intelligence (AAAI), 2023
Shijie Liu
Andrew C. Cullen
Paul Montague
S. Erfani
Benjamin I. P. Rubinstein
AAML
259
7
0
15 Aug 2023
Large-Scale Public Data Improves Differentially Private Image Generation Quality
Ruihan Wu
Chuan Guo
Kamalika Chaudhuri
311
2
0
04 Aug 2023
Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis
Conor Hassan
Roberto Salomone
Kerrie Mengersen
302
11
0
28 Jul 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis
Conference on Computer and Communications Security (CCS), 2023
Kunal Talwar
Shan Wang
Audra McMillan
Vojta Jina
Vitaly Feldman
...
Congzheng Song
Karl Tarbe
Sebastian Vogt
L. Winstrom
Shundong Zhou
FedML
370
17
0
27 Jul 2023
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering
IEEE Symposium on Security and Privacy (IEEE S&P), 2023
Ce Feng
Nuo Xu
Wujie Wen
Parv Venkitasubramaniam
Caiwen Ding
191
6
0
25 Jul 2023
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023
Meirui Jiang
Yuan Zhong
Anjie Le
Xiaoxiao Li
Qianming Dou
FedML
332
6
0
24 Jul 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
295
43
0
20 Jul 2023
Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection
Neural Information Processing Systems (NeurIPS), 2023
Eli Chien
Wei-Ning Chen
Chao Pan
Pan Li
Ayfer Özgür
O. Milenkovic
383
22
0
12 Jul 2023
Differentially Private Video Activity Recognition
IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2023
Zelun Luo
Yuliang Zou
Yijin Yang
Zane Durante
De-An Huang
Zhiding Yu
Chaowei Xiao
L. Fei-Fei
Anima Anandkumar
PICV
259
7
0
27 Jun 2023
Differentially Private Decentralized Deep Learning with Consensus Algorithms
Jasmine Bayrooti
Zhan Gao
Amanda Prorok
FedML
81
1
0
24 Jun 2023
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity
Yue Liu
Yasin Esfandiari
Mikkel N. Schmidt
T. S. Alstrøm
Sebastian U. Stich
FedML
299
6
0
23 Jun 2023
ViP: A Differentially Private Foundation Model for Computer Vision
International Conference on Machine Learning (ICML), 2023
Yaodong Yu
Maziar Sanjabi
Yi Ma
Kamalika Chaudhuri
Chuan Guo
230
17
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
252
4
0
14 Jun 2023
Gaussian Membership Inference Privacy
Neural Information Processing Systems (NeurIPS), 2023
Tobias Leemann
Martin Pawelczyk
Gjergji Kasneci
321
22
0
12 Jun 2023
Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy
Soroosh Tayebi Arasteh
Mahshad Lotfinia
T. Nolte
Marwin Saehn
P. Isfort
Christiane Kuhl
S. Nebelung
Georgios Kaissis
Daniel Truhn
MedIm
238
13
0
10 Jun 2023
Differentially Private Sharpness-Aware Training
International Conference on Machine Learning (ICML), 2023
Jinseong Park
Hoki Kim
Yujin Choi
Jaewook Lee
232
11
0
09 Jun 2023
Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting
Proceedings on Privacy Enhancing Technologies (PoPETs), 2023
Ana-Maria Cretu
Daniel Jones
Yves-Alexandre de Montjoye
Shruti Tople
AAML
205
8
0
08 Jun 2023
Differentially Private Image Classification by Learning Priors from Random Processes
Neural Information Processing Systems (NeurIPS), 2023
Xinyu Tang
Ashwinee Panda
Vikash Sehwag
Prateek Mittal
287
30
0
08 Jun 2023
Privately generating tabular data using language models
Alexandre Sablayrolles
Yue Wang
Brian Karrer
LMTD
166
5
0
07 Jun 2023
Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks
Emilie Grégoire
M. H. Chaudhary
Sam Verboven
188
5
0
07 Jun 2023
PILLAR: How to make semi-private learning more effective
Francesco Pinto
Yaxian Hu
Fanny Yang
Amartya Sanyal
235
14
0
06 Jun 2023
Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private Tuning
Annual Meeting of the Association for Computational Linguistics (ACL), 2023
Umang Gupta
Aram Galstyan
Greg Ver Steeg
213
3
0
30 May 2023
Training Private Models That Know What They Don't Know
Neural Information Processing Systems (NeurIPS), 2023
Stephan Rabanser
Anvith Thudi
Abhradeep Thakurta
Krishnamurthy Dvijotham
Nicolas Papernot
224
9
0
28 May 2023
DPFormer: Learning Differentially Private Transformer on Long-Tailed Data
Youlong Ding
Xueyang Wu
Hongya Wang
Weike Pan
318
2
0
28 May 2023
DP-SGD Without Clipping: The Lipschitz Neural Network Way
International Conference on Learning Representations (ICLR), 2023
Louis Bethune
Thomas Massena
Thibaut Boissin
Yannick Prudent
Corentin Friedrich
Franck Mamalet
A. Bellet
M. Serrurier
David Vigouroux
326
11
0
25 May 2023
Differentially Private Latent Diffusion Models
Saiyue Lyu
Michael F. Liu
Margarita Vinaroz
Mijung Park
399
32
0
25 May 2023
Personalized DP-SGD using Sampling Mechanisms
Geon Heo
Junseok Seo
Steven Euijong Whang
199
4
0
24 May 2023
Trade-Offs Between Fairness and Privacy in Language Modeling
Annual Meeting of the Association for Computational Linguistics (ACL), 2023
Cleo Matzken
Steffen Eger
Ivan Habernal
SILM
316
6
0
24 May 2023
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
International Conference on Learning Representations (ICLR), 2023
Zinan Lin
Sivakanth Gopi
Janardhan Kulkarni
Harsha Nori
Sergey Yekhanin
676
55
0
24 May 2023
Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even for Non-Convex Losses
S. Asoodeh
Mario Díaz
199
10
0
17 May 2023
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning
International Conference on Smart Computing (SMARTCOMP), 2023
Yumeki Goto
Tomoya Matsumoto
Hamada Rizk
Naoto Yanai
Hirozumi Yamaguchi
183
7
0
14 May 2023
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning
International Conference on Internet-of-Things Design and Implementation (IoTDI), 2023
Md. Adnan Arefeen
Zhouyu Li
M. Y. S. Uddin
Anupam Das
250
0
0
13 May 2023
DPMLBench: Holistic Evaluation of Differentially Private Machine Learning
Conference on Computer and Communications Security (CCS), 2023
Chengkun Wei
Ming-Hui Zhao
Zhikun Zhang
Min Chen
Wenlong Meng
Bodong Liu
Yuan-shuo Fan
Wenzhi Chen
376
17
0
10 May 2023
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training
European Interdisciplinary Cybersecurity Conference (IC), 2023
Dmitrii Usynin
Daniel Rueckert
Georgios Kaissis
FedML
352
3
0
04 May 2023
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing
IEEE Transactions on Mobile Computing (IEEE TMC), 2023
Yi Shi
Kang Wei
Li Shen
Jun Li
Xueqian Wang
Bo Yuan
Song Guo
272
8
0
02 May 2023
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
Yi Shi
Kang Wei
Li Shen
Yingqi Liu
Xueqian Wang
Bo Yuan
Dacheng Tao
FedML
265
5
0
01 May 2023
The Disharmony between BN and ReLU Causes Gradient Explosion, but is Offset by the Correlation between Activations
Inyoung Paik
Jaesik Choi
316
2
0
23 Apr 2023
ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees
Web Search and Data Mining (WSDM), 2023
Sina Sajadmanesh
D. Gática-Pérez
373
22
0
18 Apr 2023
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy
Science China Information Sciences (Sci China Inf Sci), 2023
Kang Wei
Jun Li
Chuan Ma
Ming Ding
Feng Shu
Haitao Zhao
Wen Chen
Hongbo Zhu
FedML
353
11
0
09 Apr 2023
Make Landscape Flatter in Differentially Private Federated Learning
Computer Vision and Pattern Recognition (CVPR), 2023
Yi Shi
Yingqi Liu
Kang Wei
Li Shen
Xueqian Wang
Dacheng Tao
FedML
216
88
0
20 Mar 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Journal of Artificial Intelligence Research (JAIR), 2023
Natalia Ponomareva
Hussein Hazimeh
Alexey Kurakin
Zheng Xu
Carson E. Denison
H. B. McMahan
Sergei Vassilvitskii
Steve Chien
Abhradeep Thakurta
504
240
0
01 Mar 2023
Arbitrary Decisions are a Hidden Cost of Differentially Private Training
Conference on Fairness, Accountability and Transparency (FAccT), 2023
B. Kulynych
Hsiang Hsu
Carmela Troncoso
Flavio du Pin Calmon
338
22
0
28 Feb 2023
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning
Charlie Hou
Hongyuan Zhan
Akshat Shrivastava
Sida I. Wang
S. Livshits
Giulia Fanti
Daniel Lazar
FedML
227
16
0
17 Feb 2023
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