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Deep Learning with Gaussian Differential Privacy

26 November 2019

Weijie J. Su

Papers citing "Deep Learning with Gaussian Differential Privacy"

50 / 113 papers shown

Title
Dyn-D $^2$ P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Zehan Zhu Yan Huang Xin Wang Shouling Ji Jinming Xu 96 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 86 0 0 27 Mar 2025
$(\varepsilon, δ)$ Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees Juan Felipe Gomez B. Kulynych G. Kaissis Jamie Hayes Borja Balle Antti Honkela 104 0 0 13 Mar 2025
Towards hyperparameter-free optimization with differential privacy Zhiqi Bu Ruixuan Liu 82 2 0 02 Mar 2025
The 2020 United States Decennial Census Is More Private Than You (Might) Think Buxin Su Weijie J. Su Chendi Wang 78 3 0 11 Oct 2024
Gradients Stand-in for Defending Deep Leakage in Federated Learning H. Yi H. Ren C. Hu Y. Li J. Deng Xin Xie FedML 49 0 0 11 Oct 2024
A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem Weijie J. Su 82 1 0 14 Sep 2024
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning Chendi Wang Yuqing Zhu Weijie J. Su Yu Wang AAML 95 5 0 14 May 2024
PATE-TripleGAN: Privacy-Preserving Image Synthesis with Gaussian Differential Privacy Zepeng Jiang Weiwei Ni Yifan Zhang PICV 103 1 0 19 Apr 2024
Advances in Differential Privacy and Differentially Private Machine Learning Saswat Das Subhankar Mishra 66 4 0 06 Apr 2024
Shifted Interpolation for Differential Privacy Jinho Bok Weijie Su Jason M. Altschuler 111 9 0 01 Mar 2024
Pre-training Differentially Private Models with Limited Public Data Zhiqi Bu Xinwei Zhang Mingyi Hong Sheng Zha George Karypis 114 4 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 Minlie Huang Dongsheng Li 71 10 0 26 Feb 2024
Differentially Private Fair Binary Classifications Hrad Ghoukasian S. Asoodeh FaML 79 2 0 23 Feb 2024
Decentralized Federated Learning: A Survey on Security and Privacy Ehsan Hallaji R. Razavi-Far M. Saif Boyu Wang Qiang Yang FedML 112 39 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 Yan Kang Lixin Fan Qiang Yang FedML 58 1 0 27 Dec 2023
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST) Xingyuan Zhao Fang Liu 55 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 121 1 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 96 24 0 27 Nov 2023
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release Jie Fu Qingqing Ye Haibo Hu Zhili Chen Lulu Wang Kuncan Wang Xun Ran 63 17 0 23 Nov 2023
Zero redundancy distributed learning with differential privacy Zhiqi Bu Justin Chiu Ruixuan Liu Sheng Zha George Karypis 90 8 0 20 Nov 2023
Verification of Neural Networks Local Differential Classification Privacy Roie Reshef Anan Kabaha Olga Seleznova Dana Drachsler-Cohen AAML 67 2 0 31 Oct 2023
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via $f$ -Differential Privacy Chendi Wang Buxin Su Jiayuan Ye Reza Shokri Weijie J. Su FedML 80 11 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 59 4 0 30 Oct 2023
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning Zheyuan Liu Guangyao Dou Yijun Tian Chunhui Zhang Eli Chien Ziwei Zhu MU 115 21 0 28 Oct 2023
Coupling public and private gradient provably helps optimization Ruixuan Liu Zhiqi Bu Yu Wang Sheng Zha George Karypis 68 2 0 02 Oct 2023
DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning S. Giddens Fan Liu 60 1 0 19 Sep 2023
DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass Minxin Du Xiang Yue Sherman S. M. Chow Tianhao Wang Chenyu Huang Huan Sun SILM 104 65 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 106 28 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 76 13 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 57 6 0 04 Jul 2023
DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum Xiaolan Gu Ming Li Lishuang Xiong FedML 49 5 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 95 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 129 6 0 13 Jun 2023
Personalized DP-SGD using Sampling Mechanisms Geon Heo Junseok Seo Steven Euijong Whang 55 2 0 24 May 2023
Gradient Leakage Defense with Key-Lock Module for Federated Learning Hanchi Ren Jingjing Deng Xianghua Xie Xiaoke Ma Jianfeng Ma FedML 131 2 0 06 May 2023
A Randomized Approach for Tight Privacy Accounting Jiachen T. Wang Saeed Mahloujifar Tong Wu R. Jia Prateek Mittal 106 10 0 17 Apr 2023
Fairness-aware Differentially Private Collaborative Filtering Zhenhuan Yang Yingqiang Ge Congzhe Su Dingxian Wang Xiaoting Zhao Yiming Ying FedML 39 3 0 16 Mar 2023
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$ -Differential Privacy Richeng Jin Z. Su C. Zhong Zhaoyang Zhang Tony Q.S. Quek H. Dai FedML 59 2 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 35 2 0 11 Feb 2023
$z$ -SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning Zhiwei Tang Yanmeng Wang Tsung-Hui Chang FedML 70 14 0 06 Feb 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 87 19 0 22 Jan 2023
LDL: A Defense for Label-Based Membership Inference Attacks Arezoo Rajabi D. Sahabandu Luyao Niu Bhaskar Ramasubramanian Radha Poovendran AAML 35 4 0 03 Dec 2022
Differentially Private Optimizers Can Learn Adversarially Robust Models Yuan Zhang Zhiqi Bu 81 3 0 16 Nov 2022
Privately Fine-Tuning Large Language Models with Differential Privacy R. Behnia Mohammadreza Ebrahimi Jason L. Pacheco B. Padmanabhan 123 50 0 26 Oct 2022
Differentially Private Language Models for Secure Data Sharing Justus Mattern Zhijing Jin Benjamin Weggenmann Bernhard Schoelkopf Mrinmaya Sachan SyDa 92 52 0 25 Oct 2022
TAN Without a Burn: Scaling Laws of DP-SGD Tom Sander Pierre Stock Alexandre Sablayrolles FedML 86 43 0 07 Oct 2022
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning Reza Nasirigerdeh Javad Torkzadehmahani Daniel Rueckert Georgios Kaissis 40 1 0 30 Sep 2022
Differentially Private Optimization on Large Model at Small Cost Zhiqi Bu Yu Wang Sheng Zha George Karypis 100 55 0 30 Sep 2022
Differentially Private Bias-Term Fine-tuning of Foundation Models Zhiqi Bu Yu Wang Sheng Zha George Karypis 117 48 0 30 Sep 2022