Title
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 34 12 0 18 Aug 2022
On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel Shubhi Shukla Manaar Alam Sarani Bhattacharya Debdeep Mukhopadhyay Pabitra Mitra AAML 25 2 0 01 Aug 2022
Dynamic Batch Adaptation Cristian Simionescu George Stoica Robert Herscovici ODL 19 1 0 01 Aug 2022
Widespread Underestimation of Sensitivity in Differentially Private Libraries and How to Fix It Sílvia Casacuberta Michael Shoemate Salil P. Vadhan Connor Wagaman 16 22 0 21 Jul 2022
Towards Privacy-Preserving Person Re-identification via Person Identify Shift Shuguang Dou Xinyang Jiang Qingsong Zhao Dongsheng Li Cairong Zhao 16 8 0 15 Jul 2022
Beyond Uniform Lipschitz Condition in Differentially Private Optimization Rudrajit Das Satyen Kale Zheng Xu Tong Zhang Sujay Sanghavi 24 17 0 21 Jun 2022
Shuffle Gaussian Mechanism for Differential Privacy Seng Pei Liew Tsubasa Takahashi FedML 21 2 0 20 Jun 2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu-Xiang Wang Sheng Zha George Karypis 24 69 0 14 Jun 2022
Self-Supervised Pretraining for Differentially Private Learning Arash Asadian Evan Weidner Lei Jiang PICV 27 3 0 14 Jun 2022
How unfair is private learning ? Amartya Sanyal Yaxian Hu Fanny Yang FaML FedML 33 22 0 08 Jun 2022
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent Da Yu Gautam Kamath Janardhan Kulkarni Tie-Yan Liu Jian Yin Huishuai Zhang 13 17 0 06 Jun 2022
Auditing Differential Privacy in High Dimensions with the Kernel Quantum Rényi Divergence Carles Domingo-Enrich Youssef Mroueh 21 5 0 27 May 2022
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss Jason M. Altschuler Kunal Talwar FedML 36 57 0 27 May 2022
DPSNN: A Differentially Private Spiking Neural Network with Temporal Enhanced Pooling Jihang Wang Dongcheng Zhao Guobin Shen Qian Zhang Yingda Zeng 32 2 0 24 May 2022
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation Ljubomir Rokvic Panayiotis Danassis Sai Praneeth Karimireddy Boi Faltings TDI 27 1 0 23 May 2022
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy Zhiqi Bu J. Mao Shiyun Xu 136 47 0 21 May 2022
Kernel Normalized Convolutional Networks Reza Nasirigerdeh Reihaneh Torkzadehmahani Daniel Rueckert Georgios Kaissis 21 2 0 20 May 2022
SmoothNets: Optimizing CNN architecture design for differentially private deep learning Nicolas W. Remerscheid Alexander Ziller Daniel Rueckert Georgios Kaissis 4 6 0 09 May 2022
Can collaborative learning be private, robust and scalable? Dmitrii Usynin Helena Klause Johannes C. Paetzold Daniel Rueckert Georgios Kaissis FedML MedIm 17 3 0 05 May 2022
Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation? Héber H. Arcolezi Jean-François Couchot Denis Renaud Bechara al Bouna X. Xiao AI4TS 28 5 0 01 May 2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale Soham De Leonard Berrada Jamie Hayes Samuel L. Smith Borja Balle 35 217 0 28 Apr 2022
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles Cuong Tran Keyu Zhu Ferdinando Fioretto Pascal Van Hentenryck 24 11 0 11 Apr 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 17 9 0 07 Apr 2022
ScaleSFL: A Sharding Solution for Blockchain-Based Federated Learning Evan W. R. Madill Ben Nguyen C. Leung Sara Rouhani 32 20 0 04 Apr 2022
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning Haoxiang Wang Yite Wang Ruoyu Sun Bo-wen Li 29 27 0 17 Mar 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) Jiayuan Ye Reza Shokri FedML 27 44 0 10 Mar 2022
Similarity-based Label Inference Attack against Training and Inference of Split Learning Junlin Liu Xinchen Lyu Qimei Cui Xiaofeng Tao FedML 29 26 0 10 Mar 2022
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation Sina Sajadmanesh Ali Shahin Shamsabadi A. Bellet D. Gática-Pérez 24 63 0 02 Mar 2022
Differentially private training of residual networks with scale normalisation Helena Klause Alexander Ziller Daniel Rueckert Kerstin Hammernik Georgios Kaissis 11 32 0 01 Mar 2022
Defending against Reconstruction Attacks with Rényi Differential Privacy Pierre Stock I. Shilov Ilya Mironov Alexandre Sablayrolles AAML SILM MIACV 17 39 0 15 Feb 2022
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy Rui Hu Yanmin Gong Yuanxiong Guo FedML 24 62 0 15 Feb 2022
Toward Training at ImageNet Scale with Differential Privacy Alexey Kurakin Shuang Song Steve Chien Roxana Geambasu Andreas Terzis Abhradeep Thakurta 36 100 0 28 Jan 2022
Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics T. Ryffel Francis R. Bach D. Pointcheval 23 21 0 28 Jan 2022
Plume: Differential Privacy at Scale Kareem Amin Jennifer Gillenwater Matthew Joseph Alex Kulesza Sergei Vassilvitskii 34 9 0 27 Jan 2022
Transformers in Medical Imaging: A Survey Fahad Shamshad Salman Khan Syed Waqas Zamir Muhammad Haris Khan Munawar Hayat F. Khan Huazhu Fu ViT LM&MA MedIm 111 663 0 24 Jan 2022
Synthesising Electronic Health Records: Cystic Fibrosis Patient Group E. Muller Xu Zheng Jer Hayes 36 2 0 14 Jan 2022
Differential Privacy Made Easy Muhammad Aitsam SyDa 29 8 0 01 Jan 2022
On the Importance of Difficulty Calibration in Membership Inference Attacks Lauren Watson Chuan Guo Graham Cormode Alex Sablayrolles 23 119 0 15 Nov 2021
DP-XGBoost: Private Machine Learning at Scale Cheng Cheng Wei Dai 16 8 0 25 Oct 2021
Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning? Guy Heller Ethan Fetaya BDL 30 3 0 11 Oct 2021
NanoBatch Privacy: Enabling fast Differentially Private learning on the IPU Edward H. Lee M. M. Krell Alexander Tsyplikhin Victoria Rege E. Colak Kristen W. Yeom FedML 21 0 0 24 Sep 2021
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization P. Subramani Nicholas Vadivelu Gautam Kamath 18 83 0 18 Oct 2020
Individual Privacy Accounting via a Renyi Filter Vitaly Feldman Tijana Zrnic 59 86 0 25 Aug 2020
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning Xinjian Luo Xiangqi Zhu FedML 73 25 0 27 Apr 2020
Efficient Per-Example Gradient Computations Ian Goodfellow 186 74 0 07 Oct 2015