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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
An Optimization Framework for Differentially Private Sparse Fine-Tuning
Mehdi Makni
Kayhan Behdin
Gabriel Afriat
Zheng Xu
Sergei Vassilvitskii
Natalia Ponomareva
Hussein Hazimeh
Rahul Mazumder
314
1
0
17 Mar 2025
PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models
International Conference on Learning Representations (ICLR), 2025
Kyeongkook Seo
Dong-Jun Han
Jaejun Yoo
497
3
0
11 Mar 2025
Video-DPRP: A Differentially Private Approach for Visual Privacy-Preserving Video Human Activity Recognition
Allassan Tchangmena A Nken
Susan Mckeever
Peter Corcoran
Ihsan Ullah
PICV
399
0
0
03 Mar 2025
An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses
Hao Liang
Feiyu Xiong
Xinlei He
Kaishun He
Hong Xing
341
2
0
25 Feb 2025
Differentially Private Federated Learning With Time-Adaptive Privacy Spending
International Conference on Learning Representations (ICLR), 2025
Shahrzad Kiani
Nupur Kulkarni
Adam Dziedzic
S. Draper
Franziska Boenisch
FedML
551
5
0
25 Feb 2025
Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study
Proceedings on Privacy Enhancing Technologies (PoPETs), 2024
Ayana Moshruba
Ihsen Alouani
Maryam Parsa
AAML
301
5
0
24 Feb 2025
Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning
Raghav Singhal
Kaustubh Ponkshe
Rohit Vartak
Lav R. Varshney
Praneeth Vepakomma
FedML
273
7
0
21 Feb 2025
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
AAAI Conference on Artificial Intelligence (AAAI), 2024
Yanming Liu
Xinyue Peng
Yuwei Zhang
Xiaolan Ke
Songhang Deng
...
Sheng Cheng
Xun Wang
Yuxiang Cai
Xuhong Zhang
Xuhong Zhang
617
0
0
21 Feb 2025
The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
Matthieu Meeus
Lukas Wutschitz
Santiago Zanella Béguelin
Shruti Tople
Reza Shokri
445
7
0
19 Feb 2025
Differentially Private Prototypes for Imbalanced Transfer Learning
AAAI Conference on Artificial Intelligence (AAAI), 2024
Dariush Wahdany
Matthew Jagielski
Adam Dziedzic
Franziska Boenisch
344
0
0
17 Feb 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients
David Rodríguez-Vítores
Clément Lalanne
Jean-Michel Loubes
FedML
482
0
0
03 Feb 2025
Balls-and-Bins Sampling for DP-SGD
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Lynn Chua
Badih Ghazi
Charlie Harrison
Ethan Leeman
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
Amer Sinha
Chiyuan Zhang
344
8
0
21 Dec 2024
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios
Sangyeon Yoon
Wonje Jeung
Albert No
400
1
0
02 Dec 2024
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models
Olivia Ma
Jonathan Passerat-Palmbach
Dmitrii Usynin
373
2
0
24 Nov 2024
Attribute Inference Attacks for Federated Regression Tasks
AAAI Conference on Artificial Intelligence (AAAI), 2024
Francesco Diana
Othmane Marfoq
Chuan Xu
Giovanni Neglia
F. Giroire
Eoin Thomas
AAML
1.2K
1
0
19 Nov 2024
Scalable DP-SGD: Shuffling vs. Poisson Subsampling
Neural Information Processing Systems (NeurIPS), 2024
Lynn Chua
Badih Ghazi
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
Amer Sinha
Chiyuan Zhang
264
18
0
06 Nov 2024
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Marlon Tobaben
Mohamed Ali Souibgui
Rubèn Pérez Tito
Khanh Nguyen
Raouf Kerkouche
...
Josep Lladós
Ernest Valveny
Antti Honkela
Mario Fritz
Dimosthenis Karatzas
FedML
376
1
0
06 Nov 2024
Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight
Tao Huang
Qingyu Huang
Xin Shi
Jiayang Meng
Guolong Zheng
Xu Yang
Xun Yi
245
0
0
05 Nov 2024
R+R:Understanding Hyperparameter Effects in DP-SGD
Asia-Pacific Computer Systems Architecture Conference (ACSA), 2024
Felix Morsbach
J. Reubold
T. Strufe
241
1
0
04 Nov 2024
Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers
Neural Information Processing Systems (NeurIPS), 2024
Gavia Gray
Aman Tiwari
Shane Bergsma
Joel Hestness
357
2
0
01 Nov 2024
Does Differential Privacy Impact Bias in Pretrained NLP Models?
Md. Khairul Islam
Andrew Wang
Tianhao Wang
Yangfeng Ji
Judy Fox
Jieyu Zhao
AI4CE
215
1
0
24 Oct 2024
DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation
IEEE Transactions on Medical Imaging (IEEE TMI), 2024
Meilu Zhu
Axiu Mao
Jun Liu
Yixuan Yuan
219
7
0
16 Oct 2024
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models
Annual Meeting of the Association for Computational Linguistics (ACL), 2024
Sajjad Ghiasvand
Yifan Yang
Zhiyu Xue
Mahnoosh Alizadeh
Zheng Zhang
Ramtin Pedarsani
FedML
563
7
0
16 Oct 2024
Privately Learning from Graphs with Applications in Fine-tuning Large Language Models
Haoteng Yin
Rongzhe Wei
Eli Chien
P. Li
444
1
0
10 Oct 2024
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024
Wenhao Wang
Xiaoyu Liang
Rui Ye
Jingyi Chai
Siheng Chen
Yanfeng Wang
SyDa
342
9
0
08 Oct 2024
Near Exact Privacy Amplification for Matrix Mechanisms
International Conference on Learning Representations (ICLR), 2024
Christopher A. Choquette-Choo
Arun Ganesh
Saminul Haque
Thomas Steinke
Abhradeep Thakurta
363
13
0
08 Oct 2024
Adaptively Private Next-Token Prediction of Large Language Models
James Flemings
Meisam Razaviyayn
Murali Annavaram
430
3
0
02 Oct 2024
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Kristian Schwethelm
Johannes Kaiser
Jonas Kuntzer
Mehmet Yigitsoy
Daniel Rueckert
Georgios Kaissis
321
0
0
01 Oct 2024
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks
Tran Viet Khoa
Mohammad Abu Alsheikh
Yibeltal Alem
D. Hoang
FedML
134
3
0
08 Sep 2024
Private Means and the Curious Incident of the Free Lunch
Jack Fitzsimons
James Honaker
Michael Shoemate
Vikrant Singhal
374
3
0
19 Aug 2024
Synthetic Trajectory Generation Through Convolutional Neural Networks
Jesse Merhi
Erik Buchholz
S. Kanhere
215
0
0
24 Jul 2024
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models
Deniz Daum
Richard Osuala
Anneliese Riess
Georgios Kaissis
Julia A. Schnabel
Maxime Di Folco
MedIm
209
4
0
23 Jul 2024
Weights Shuffling for Improving DPSGD in Transformer-based Models
Jungang Yang
Zhe Ji
Liyao Xiang
300
1
0
22 Jul 2024
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
Luca Corbucci
Mikko A. Heikkilä
David Solans Noguero
Anna Monreale
Nicolas Kourtellis
FedML
382
6
0
21 Jul 2024
Defining 'Good': Evaluation Framework for Synthetic Smart Meter Data
Sheng Chai
Gus Chadney
Charlot Avery
Phil Grunewald
Pascal Van Hentenryck
P. Donti
187
8
0
16 Jul 2024
Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex composite losses
Weiwei Kong
Mónica Ribero
385
5
0
07 Jul 2024
Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature
Deepak Ravikumar
Efstathia Soufleri
Kaushik Roy
180
4
0
03 Jul 2024
Attack-Aware Noise Calibration for Differential Privacy
B. Kulynych
Juan Felipe Gomez
G. Kaissis
Flavio du Pin Calmon
Carmela Troncoso
335
17
0
02 Jul 2024
Noise-Aware Differentially Private Regression via Meta-Learning
Ossi Raisa
Stratis Markou
Matthew Ashman
W. Bruinsma
Marlon Tobaben
Antti Honkela
Richard Turner
450
1
0
12 Jun 2024
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
Charlie Hou
Akshat Shrivastava
Hongyuan Zhan
Rylan Conway
Trang Le
Adithya Sagar
Giulia Fanti
Daniel Lazar
293
25
0
05 Jun 2024
Differentially Private Tabular Data Synthesis using Large Language Models
Toan V. Tran
Li Xiong
SyDa
282
12
0
03 Jun 2024
Differentially Private Fine-Tuning of Diffusion Models
Yu-Lin Tsai
Yizhe Li
Zekai Chen
Po-yu Chen
Chia-Mu Yu
Xuebin Ren
Francois Buet-Golfouse
210
6
0
03 Jun 2024
Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients
Weijun Li
Xingliang Yuan
Mark Dras
PILM
263
4
0
03 Jun 2024
Delving into Differentially Private Transformer
Youlong Ding
Xueyang Wu
Yining Meng
Yonggang Luo
Hao Wang
Weike Pan
449
10
0
28 May 2024
Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings
Robert Wolfe
Isaac Slaughter
Bin Han
Bingbing Wen
Yiwei Yang
...
Bernease Herman
E. Brown
Zening Qu
Nicholas Weber
Bill Howe
296
11
0
27 May 2024
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition
C. Lebeda
Matthew Regehr
Gautam Kamath
Thomas Steinke
381
13
0
27 May 2024
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data
Haoran Li
Xinyuan Zhao
Dadi Guo
Hanlin Gu
Huiping Zhuang
Yuxing Han
Yangqiu Song
Lixin Fan
Qiang Yang
195
4
0
23 May 2024
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
Neural Information Processing Systems (NeurIPS), 2024
Meenatchi Sundaram Muthu Selva Annamalai
Emiliano De Cristofaro
301
18
0
23 May 2024
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
International Conference on Learning Representations (ICLR), 2024
Tudor Cebere
A. Bellet
Nicolas Papernot
474
16
0
23 May 2024
Banded Square Root Matrix Factorization for Differentially Private Model Training
Nikita P. Kalinin
Christoph H. Lampert
365
10
0
22 May 2024
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