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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
Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models
IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2024
Matías Mendieta
Guangyu Sun
Chong Chen
160
6
0
02 May 2024
Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data
Vikhyat Agrawal
Sunil Vasu Kalmady
Venkataseetharam Manoj Malipeddi
Manisimha Manthena
Weijie Sun
Saiful Islam
Abram Hindle
Padma Kaul
Russell Greiner
FedML
276
12
0
26 Apr 2024
A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation
Yiwen Tu
Pingbang Hu
Jiaqi W. Ma
MU
485
2
0
17 Apr 2024
LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
Juntaek Lim
Youngeun Kwon
Ranggi Hwang
Kiwan Maeng
Edward Suh
Minsoo Rhu
SyDa
205
1
0
12 Apr 2024
pfl-research: simulation framework for accelerating research in Private Federated Learning
Neural Information Processing Systems (NeurIPS), 2024
Filip Granqvist
Congzheng Song
Áine Cahill
Rogier van Dalen
Martin Pelikan
Yi Sheng Chan
Xiaojun Feng
Natarajan Krishnaswami
Vojta Jina
Mona Chitnis
FedML
228
13
0
09 Apr 2024
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
Shanglun Feng
Florian Tramèr
SILM
262
30
0
30 Mar 2024
How Private are DP-SGD Implementations?
Lynn Chua
Badih Ghazi
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
Amer Sinha
Chiyuan Zhang
397
21
0
26 Mar 2024
TablePuppet: A Generic Framework for Relational Federated Learning
Lijie Xu
Chulin Xie
Yiran Guo
Gustavo Alonso
Yue Liu
Guoliang Li
Wei Wang
Wentao Wu
Ce Zhang
FedML
241
0
0
23 Mar 2024
Differentially Private Next-Token Prediction of Large Language Models
North American Chapter of the Association for Computational Linguistics (NAACL), 2024
James Flemings
Meisam Razaviyayn
Murali Annavaram
464
21
0
22 Mar 2024
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning
Jonathan Lebensold
Maziar Sanjabi
Pietro Astolfi
Adriana Romero Soriano
Kamalika Chaudhuri
Mike Rabbat
Chuan Guo
DiffM
376
8
0
21 Mar 2024
Improving LoRA in Privacy-preserving Federated Learning
International Conference on Learning Representations (ICLR), 2024
Youbang Sun
Zitao Li
Yaliang Li
Bolin Ding
379
127
0
18 Mar 2024
Sentinel-Guided Zero-Shot Learning: A Collaborative Paradigm without Real Data Exposure
Fan Wan
Xingyu Miao
Haoran Duan
Jingjing Deng
Rui Gao
Yang Long
VLM
231
6
0
14 Mar 2024
DP-TLDM: Differentially Private Tabular Latent Diffusion Model
ARES (ARES), 2024
Chaoyi Zhu
Jiayi Tang
Juan F. Pérez
Marten van Dijk
Marten van Dijk
284
5
0
12 Mar 2024
Inverse-Free Fast Natural Gradient Descent Method for Deep Learning
Xinwei Ou
Ce Zhu
Xiaolin Huang
Yipeng Liu
ODL
262
0
0
06 Mar 2024
Differentially Private Representation Learning via Image Captioning
Tom Sander
Yaodong Yu
Maziar Sanjabi
Alain Durmus
Yi-An Ma
Kamalika Chaudhuri
Chuan Guo
276
7
0
04 Mar 2024
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
Qi Tan
Qi Li
Yi Zhao
Zhuotao Liu
Xiaobing Guo
Ke Xu
FedML
246
9
0
02 Mar 2024
Differentially Private Knowledge Distillation via Synthetic Text Generation
James Flemings
Murali Annavaram
SyDa
409
18
0
01 Mar 2024
Characterizing the Training Dynamics of Private Fine-tuning with Langevin diffusion
Shuqi Ke
Charlie Hou
Giulia Fanti
Sewoong Oh
239
5
0
29 Feb 2024
Pre-training Differentially Private Models with Limited Public Data
Zhiqi Bu
Xinwei Zhang
Mingyi Hong
Sheng Zha
George Karypis
302
6
0
28 Feb 2024
Unveiling Privacy, Memorization, and Input Curvature Links
Deepak Ravikumar
Efstathia Soufleri
Abolfazl Hashemi
Kaushik Roy
291
13
0
28 Feb 2024
Differentially Private Fair Binary Classifications
Hrad Ghoukasian
S. Asoodeh
FaML
233
6
0
23 Feb 2024
Closed-Form Bounds for DP-SGD against Record-level Inference
Giovanni Cherubin
Boris Köpf
Andrew Paverd
Shruti Tople
Lukas Wutschitz
Santiago Zanella Béguelin
193
2
0
22 Feb 2024
PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
Mishaal Kazmi
H. Lautraite
Alireza Akbari
Mauricio Soroco
Qiaoyue Tang
Tao Wang
Sébastien Gambs
Mathias Lécuyer
250
18
0
12 Feb 2024
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
Yuecheng Li
Lele Fu
Tong Wang
Jian Lou
Bin Chen
Lei Yang
Zibin Zheng
Zibin Zheng
Chuan Chen
FedML
350
6
0
10 Feb 2024
Privacy Profiles for Private Selection
Antti Koskela
Rachel Redberg
Yu-Xiang Wang
309
2
0
09 Feb 2024
De-amplifying Bias from Differential Privacy in Language Model Fine-tuning
Sanjari Srivastava
Piotr (Peter) Mardziel
Zhikhun Zhang
Archana Ahlawat
Anupam Datta
John C. Mitchell
218
4
0
07 Feb 2024
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation
Ossi Raisa
Hibiki Ito
Antti Honkela
243
7
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
206
25
0
31 Jan 2024
Cross-silo Federated Learning with Record-level Personalized Differential Privacy
Junxu Liu
Jian Lou
Li Xiong
Jinfei Liu
Xiaofeng Meng
470
26
0
29 Jan 2024
Private Fine-tuning of Large Language Models with Zeroth-order Optimization
Xinyu Tang
Ashwinee Panda
Milad Nasr
Saeed Mahloujifar
Prateek Mittal
566
40
0
09 Jan 2024
Facebook Report on Privacy of fNIRS data
Md. Imran Hossen
Sai Venkatesh Chilukoti
Liqun Shan
Vijay Srinivas Tida
X. Hei
118
0
0
01 Jan 2024
AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning
Hideaki Takahashi
SILM
273
2
0
29 Dec 2023
An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms
Sofia Zahri
Hajar Bennouri
A. Abdelmoniem
FedML
142
5
0
24 Dec 2023
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift
Pratiksha Thaker
Amrith Rajagopal Setlur
Zhiwei Steven Wu
Virginia Smith
421
6
0
24 Dec 2023
DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)
Qiaoyue Tang
Frederick Shpilevskiy
Mathias Lécuyer
202
28
0
21 Dec 2023
Federated learning with differential privacy and an untrusted aggregator
Kunlong Liu
Trinabh Gupta
293
2
0
17 Dec 2023
Automated discovery of trade-off between utility, privacy and fairness in machine learning models
Bogdan Ficiu
Neil D. Lawrence
Andrei Paleyes
200
2
0
27 Nov 2023
DP-NMT: Scalable Differentially-Private Machine Translation
Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2023
Timour Igamberdiev
Doan Nam Long Vu
Felix Künnecke
Zhuo Yu
Jannik Holmer
Ivan Habernal
236
8
0
24 Nov 2023
Zero redundancy distributed learning with differential privacy
Zhiqi Bu
Justin Chiu
Ruixuan Liu
Sheng Zha
George Karypis
243
9
0
20 Nov 2023
Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent
Lauren Watson
Eric Gan
Mohan Dantam
Baharan Mirzasoleiman
Rik Sarkar
186
1
0
12 Nov 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
347
17
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
203
5
0
30 Oct 2023
DP-SGD with weight clipping
Antoine Barczewski
Jan Ramon
431
1
0
27 Oct 2023
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
International Conference on Learning Representations (ICLR), 2023
Harsh Chaudhari
Giorgio Severi
Alina Oprea
Jonathan R. Ullman
285
7
0
05 Oct 2023
Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study
IEEE International Conference on Computer Vision (ICCV), 2023
Myeongseob Ko
Ming Jin
Chenguang Wang
Ruoxi Jia
239
42
0
29 Sep 2023
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
Martin Pelikan
Sheikh Shams Azam
Vitaly Feldman
Jan Honza Silovsky
Kunal Talwar
Christopher G. Brinton
Tatiana Likhomanenko
577
7
0
29 Sep 2023
Evaluating the Usability of Differential Privacy Tools with Data Practitioners
Ivoline C. Ngong
Brad Stenger
Joseph P. Near
Yuanyuan Feng
266
20
0
24 Sep 2023
Communication Efficient Private Federated Learning Using Dithering
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023
Burak Hasircioglu
Deniz Gunduz
FedML
276
13
0
14 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
389
97
0
13 Sep 2023
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning
IEEE Conference on Decision and Control (CDC), 2023
Parham Gohari
Matthew T. Hale
Ufuk Topcu
OffRL
206
3
0
13 Sep 2023
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