SmoothNets: Optimizing CNN architecture design for differentially
private deep learning

SmoothNets: Optimizing CNN architecture design for differentially private deep learning

9 May 2022

Nicolas W. Remerscheid

Alexander Ziller

Daniel Rueckert

Georgios Kaissis

Papers citing "SmoothNets: Optimizing CNN architecture design for differentially private deep learning"

6 / 6 papers shown

Title
R+R:Understanding Hyperparameter Effects in DP-SGD Felix Morsbach J. Reubold T. Strufe 36 0 0 04 Nov 2024
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning Reza Nasirigerdeh Javad Torkzadehmahani Daniel Rueckert Georgios Kaissis 16 1 0 30 Sep 2022
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks Dmitrii Usynin Daniel Rueckert Georgios Kaissis SILM AAML 28 17 0 01 Mar 2022
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates Friedrich Dörmann Osvald Frisk L. Andersen Christian Fischer Pedersen FedML 59 25 0 12 Oct 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 168 350 0 25 Sep 2021
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 147 178 0 28 Jul 2020