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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
Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacy Kai Packhauser Sebastian Gündel Florian Thamm Felix Denzinger Andreas Maier 53 3 0 23 Sep 2022
Majority Vote for Distributed Differentially Private Sign Selection Weidong Liu Jiyuan Tu Xiaojun Mao Xinyu Chen FedML 61 1 0 08 Sep 2022
Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection Vijay Srinivas Tida Sonya Hsu X. Hei MedIm 104 5 0 07 Sep 2022
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy Wenqiang Ruan Ming Xu Wenjing Fang Li Wang Lei Wang Wei Han 76 14 0 18 Aug 2022
Accelerating Vertical Federated Learning Dongqi Cai Tao Fan Yan Kang Lixin Fan Mengwei Xu Shangguang Wang Qiang Yang FedML 61 8 0 23 Jul 2022
Faster Privacy Accounting via Evolving Discretization Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi 104 14 0 10 Jul 2022
Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization Xiaodong Yang Huishuai Zhang Wei Chen Tie-Yan Liu 74 38 0 27 Jun 2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu Wang Sheng Zha George Karypis 121 72 0 14 Jun 2022
Self-Supervised Pretraining for Differentially Private Learning Arash Asadian Evan Weidner Lei Jiang PICV 73 3 0 14 Jun 2022
Analytical Composition of Differential Privacy via the Edgeworth Accountant Hua Wang Sheng-yang Gao Huanyu Zhang Milan Shen Weijie J. Su FedML 73 22 0 09 Jun 2022
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent Da Yu Gautam Kamath Janardhan Kulkarni Tie-Yan Liu Jian Yin Huishuai Zhang 141 22 0 06 Jun 2022
DPSNN: A Differentially Private Spiking Neural Network with Temporal Enhanced Pooling Jihang Wang Dongcheng Zhao Guobin Shen Qian Zhang Yingda Zeng 82 2 0 24 May 2022
$Privacy accounting $\varepsilon$conomics: Improving differential privacy composition via a posteriori bounds$ Privacy accounting $\varepsilon$ conomics: Improving differential privacy composition via a posteriori bounds Valentin Hartmann Vincent Bindschaedler Alexander Bentkamp Robert West 52 1 0 06 May 2022
What You See is What You Get: Principled Deep Learning via Distributional Generalization B. Kulynych Yao-Yuan Yang Yaodong Yu Jarosław Błasiok Preetum Nakkiran OOD 71 10 0 07 Apr 2022
Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach W. Boulila Adel Ammar Bilel Benjdira Anis Koubaa 42 13 0 15 Mar 2022
No Free Lunch Theorem for Security and Utility in Federated Learning Xiaojin Zhang Hanlin Gu Lixin Fan Kai Chen Qiang Yang FedML 95 66 0 11 Mar 2022
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release Donghao Li Yang Cao Yuan Yao 83 2 0 14 Feb 2022
Backpropagation Clipping for Deep Learning with Differential Privacy Timothy Stevens Ivoline C. Ngong David Darais Calvin Hirsch David Slater Joseph P. Near 67 10 0 10 Feb 2022
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges Nuria Rodríguez-Barroso Daniel Jiménez López M. V. Luzón Francisco Herrera Eugenio Martínez-Cámara FedML 85 228 0 20 Jan 2022
DP-FP: Differentially Private Forward Propagation for Large Models Jian Du Haitao Mi 82 5 0 29 Dec 2021
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors Timothy Stevens Christian Skalka C. Vincent J. Ring Samuel Clark Joseph P. Near FedML 75 72 0 13 Dec 2021
Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems Jiankai Jin Eleanor McMurtry Benjamin I. P. Rubinstein O. Ohrimenko 76 38 0 10 Dec 2021
TEE-based Selective Testing of Local Workers in Federated Learning Systems Wensheng Zhang Trent Muhr FedML 58 2 0 04 Nov 2021
Dynamic Differential-Privacy Preserving SGD Jian Du Song Li Xiangyi Chen Siheng Chen Mingyi Hong 90 33 0 30 Oct 2021
Differentially Private Federated Bayesian Optimization with Distributed Exploration Zhongxiang Dai K. H. Low Patrick Jaillet FedML 75 43 0 27 Oct 2021
DP-XGBoost: Private Machine Learning at Scale Cheng Cheng Wei Dai 42 9 0 25 Oct 2021
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy Xiaolan Gu Ming Li Lishuang Xiong FedML 52 10 0 22 Oct 2021
Noise-Augmented Privacy-Preserving Empirical Risk Minimization with Dual-purpose Regularizer and Privacy Budget Retrieval and Recycling Yinan Li Fang Liu 23 3 0 16 Oct 2021
Adaptive Differentially Private Empirical Risk Minimization Xiaoxia Wu Lingxiao Wang Irina Cristali Quanquan Gu Rebecca Willett 156 6 0 14 Oct 2021
Task-aware Privacy Preservation for Multi-dimensional Data Jiangnan Cheng A. Tang Sandeep P. Chinchali 68 7 0 05 Oct 2021
Federated Deep Learning with Bayesian Privacy Hanlin Gu Lixin Fan Bowen Li Jie Li Yan Kang Yuan Yao Qiang Yang FedML 162 23 0 27 Sep 2021
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index Georgios Kaissis Moritz Knolle F. Jungmann Alexander Ziller Dmitrii Usynin Daniel Rueckert 59 1 0 22 Sep 2021
Data-Free Evaluation of User Contributions in Federated Learning Hongtao Lv Zhenzhe Zheng Tie-Mei Luo Fan Wu Shaojie Tang Lifeng Hua Rongfei Jia Chengfei Lv FedML 59 27 0 24 Aug 2021
Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability Qiyiwen Zhang Zhiqi Bu Kan Chen Qi Long BDL UQCV 65 11 0 18 Jul 2021
Privacy Amplification via Iteration for Shuffled and Online PNSGD Matteo Sordello Zhiqi Bu Jinshuo Dong FedML 54 7 0 20 Jun 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting Harsha Nori R. Caruana Zhiqi Bu J. Shen Janardhan Kulkarni 83 38 0 17 Jun 2021
On the Convergence and Calibration of Deep Learning with Differential Privacy Zhiqi Bu Hua Wang Zongyu Dai Qi Long 92 31 0 15 Jun 2021
Numerical Composition of Differential Privacy Sivakanth Gopi Y. Lee Lukas Wutschitz 86 183 0 05 Jun 2021
Oneshot Differentially Private Top-k Selection Gang Qiao Weijie J. Su Li Zhang 89 34 0 18 May 2021
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning Hanchi Ren Jingjing Deng Xianghua Xie SILM AAML FedML 103 103 0 02 May 2021
Rejoinder: Gaussian Differential Privacy Jinshuo Dong Aaron Roth Weijie J. Su 38 2 0 05 Apr 2021
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation Chris Waites Rachel Cummings 52 17 0 25 Mar 2021
Differentially private inference via noisy optimization Marco Avella-Medina Casey Bradshaw Po-Ling Loh FedML 90 31 0 19 Mar 2021
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models Fatemehsadat Mireshghallah Huseyin A. Inan Marcello Hasegawa Victor Rühle Taylor Berg-Kirkpatrick Robert Sim 47 43 0 12 Mar 2021
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT A. Koskela Antti Honkela 59 20 0 24 Feb 2021
Federated $f$ -Differential Privacy Qinqing Zheng Shuxiao Chen Qi Long Weijie J. Su FedML 149 55 0 22 Feb 2021
On the robustness of randomized classifiers to adversarial examples Rafael Pinot Laurent Meunier Florian Yger Cédric Gouy-Pailler Y. Chevaleyre Jamal Atif AAML 75 14 0 22 Feb 2021
Deep Learning with Label Differential Privacy Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Chiyuan Zhang 118 153 0 11 Feb 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections Zhiqi Bu Sivakanth Gopi Janardhan Kulkarni Y. Lee J. Shen U. Tantipongpipat FedML 109 42 0 05 Feb 2021
A Theoretical Perspective on Differentially Private Federated Multi-task Learning Huiwen Wu Cen Chen Li Wang FedML 54 12 0 14 Nov 2020