Toward Training at ImageNet Scale with Differential Privacy

28 January 2022

Papers citing "Toward Training at ImageNet Scale with Differential Privacy"

50 / 82 papers shown

Title
Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy Rushabh Solanki Meghana Bhange Ulrich Aïvodji Elliot Creager 29 0 0 09 May 2025
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 51 0 0 17 Mar 2025
Empirical Privacy Variance Yuzheng Hu Fan Wu Ruicheng Xian Yuhang Liu Lydia Zakynthinou Pritish Kamath Chiyuan Zhang David A. Forsyth 62 0 0 16 Mar 2025
Data Efficient Subset Training with Differential Privacy Ninad Jayesh Gandhi Moparthy Venkata Subrahmanya Sri Harsha 53 0 0 09 Mar 2025
Towards hyperparameter-free optimization with differential privacy Zhiqi Bu Ruixuan Liu 24 1 0 02 Mar 2025
Smoothed Normalization for Efficient Distributed Private Optimization Egor Shulgin Sarit Khirirat Peter Richtárik FedML 82 0 0 20 Feb 2025
The Impact of Generalization Techniques on the Interplay Among Privacy, Utility, and Fairness in Image Classification Ahmad Hassanpour Amir Zarei Khawla Mallat Anderson Santana de Oliveira Bian Yang 77 0 0 16 Dec 2024
Preserving Expert-Level Privacy in Offline Reinforcement Learning Navodita Sharma Vishnu Vinod Abhradeep Thakurta Alekh Agarwal Borja Balle Christoph Dann A. Raghuveer OffRL 81 0 0 18 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 39 0 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 26 0 0 05 Nov 2024
R+R:Understanding Hyperparameter Effects in DP-SGD Felix Morsbach J. Reubold T. Strufe 34 0 0 04 Nov 2024
Masked Differential Privacy David Schneider Sina Sajadmanesh Vikash Sehwag Saquib Sarfraz Rainer Stiefelhagen Lingjuan Lyu Vivek Sharma 28 0 0 22 Oct 2024
Training Large ASR Encoders with Differential Privacy Geeticka Chauhan Steve Chien Om Thakkar Abhradeep Thakurta Arun Narayanan 33 1 0 21 Sep 2024
Scalable Differential Privacy Mechanisms for Real-Time Machine Learning Applications Jessica Smith David Williams Emily Brown 26 0 0 16 Sep 2024
Weights Shuffling for Improving DPSGD in Transformer-based Models Jungang Yang Zhe Ji Liyao Xiang 37 0 0 22 Jul 2024
Banded Square Root Matrix Factorization for Differentially Private Model Training Nikita Kalinin Christoph H. Lampert 26 5 0 22 May 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 31 0 0 12 Apr 2024
DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training Zihao Wang Rui Zhu Dongruo Zhou Zhikun Zhang John C. Mitchell Haixu Tang XiaoFeng Wang AAML 43 6 0 05 Mar 2024
Differentially Private Representation Learning via Image Captioning Tom Sander Yaodong Yu Maziar Sanjabi Alain Durmus Yi-An Ma Kamalika Chaudhuri Chuan Guo 58 3 0 04 Mar 2024
Pre-training Differentially Private Models with Limited Public Data Zhiqi Bu Xinwei Zhang Mingyi Hong Sheng Zha George Karypis 77 3 0 28 Feb 2024
Privacy-Preserving Instructions for Aligning Large Language Models Da Yu Peter Kairouz Sewoong Oh Zheng Xu 32 17 0 21 Feb 2024
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation Gavin Brown Krishnamurthy Dvijotham Georgina Evans Daogao Liu Adam D. Smith Abhradeep Thakurta 39 3 0 21 Feb 2024
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift Pratiksha Thaker Amrith Rajagopal Setlur Zhiwei Steven Wu Virginia Smith 37 2 0 24 Dec 2023
Privacy-Aware Document Visual Question Answering Rubèn Pérez Tito Khanh Nguyen Marlon Tobaben Raouf Kerkouche Mohamed Ali Souibgui ... Lei Kang Ernest Valveny Antti Honkela Mario Fritz Dimosthenis Karatzas 35 13 0 15 Dec 2023
Optimal Unbiased Randomizers for Regression with Label Differential Privacy Ashwinkumar Badanidiyuru Badih Ghazi Pritish Kamath Ravi Kumar Ethan Leeman Pasin Manurangsi A. Varadarajan Chiyuan Zhang 34 4 0 09 Dec 2023
Zero redundancy distributed learning with differential privacy Zhiqi Bu Justin Chiu Ruixuan Liu Sheng Zha George Karypis 40 8 0 20 Nov 2023
Sparsity-Preserving Differentially Private Training of Large Embedding Models Badih Ghazi Yangsibo Huang Pritish Kamath Ravi Kumar Pasin Manurangsi Amer Sinha Chiyuan Zhang 21 2 0 14 Nov 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 27 10 0 19 Oct 2023
A Comprehensive Study of Privacy Risks in Curriculum Learning Joann Qiongna Chen Xinlei He Zheng Li Yang Zhang Zhou Li 48 2 0 16 Oct 2023
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning Harsh Chaudhari Giorgio Severi Alina Oprea Jonathan R. Ullman 25 5 0 05 Oct 2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification Leonard Berrada Soham De J. Shen Jamie Hayes Robert Stanforth David Stutz Pushmeet Kohli Samuel L. Smith Borja Balle 21 13 0 21 Aug 2023
DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation Rodrigo Castellon Achintya Gopal Brian Bloniarz David S. Rosenberg 16 7 0 19 Jul 2023
Differentially Private Video Activity Recognition Zelun Luo Yuliang Zou Yijin Yang Zane Durante De-An Huang Zhiding Yu Chaowei Xiao L. Fei-Fei Anima Anandkumar PICV 29 3 0 27 Jun 2023
Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification Kamil Adamczewski Yingchen He Mijung Park 21 2 0 19 Jun 2023
ViP: A Differentially Private Foundation Model for Computer Vision Yaodong Yu Maziar Sanjabi Y. Ma Kamalika Chaudhuri Chuan Guo 16 12 0 15 Jun 2023
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 46 11 0 06 Jun 2023
Clip21: Error Feedback for Gradient Clipping Sarit Khirirat Eduard A. Gorbunov Samuel Horváth Rustem Islamov Fakhri Karray Peter Richtárik 27 10 0 30 May 2023
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zi-Han Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 32 19 0 23 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 32 11 0 10 May 2023
Practical Differentially Private and Byzantine-resilient Federated Learning Zihang Xiang Tianhao Wang Wanyu Lin Di Wang FedML 31 21 0 15 Apr 2023
Differential Privacy Meets Neural Network Pruning Kamil Adamczewski Mijung Park SyDa 29 5 0 08 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 94 167 0 01 Mar 2023
Why Is Public Pretraining Necessary for Private Model Training? Arun Ganesh Mahdi Haghifam Milad Nasr Sewoong Oh Thomas Steinke Om Thakkar Abhradeep Thakurta Lun Wang 21 36 0 19 Feb 2023
An Empirical Analysis of Fairness Notions under Differential Privacy Anderson Santana de Oliveira Caelin Kaplan Khawla Mallat Tanmay Chakraborty FedML 13 7 0 06 Feb 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging Soroosh Tayebi Arasteh Alexander Ziller Christiane Kuhl Marcus R. Makowski S. Nebelung R. Braren Daniel Rueckert Daniel Truhn Georgios Kaissis MedIm 34 17 0 03 Feb 2023
On the Efficacy of Differentially Private Few-shot Image Classification Marlon Tobaben Aliaksandra Shysheya J. Bronskill Andrew J. Paverd Shruti Tople Santiago Zanella Béguelin Richard E. Turner Antti Honkela 33 11 0 02 Feb 2023
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations Hui-Po Wang Dingfan Chen Raouf Kerkouche Mario Fritz FedML DD 18 4 0 02 Feb 2023
Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models Florian A. Hölzl Daniel Rueckert Georgios Kaissis 26 4 0 30 Jan 2023
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining Florian Tramèr Gautam Kamath Nicholas Carlini SILM 40 67 0 13 Dec 2022
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping Jiyan He Xuechen Li Da Yu Huishuai Zhang Janardhan Kulkarni Y. Lee A. Backurs Nenghai Yu Jiang Bian 19 46 0 03 Dec 2022