Reconciling AI Performance and Data Reconstruction Resilience for
Medical Imaging

Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging

5 December 2023

Alexander Ziller

Tamara T. Mueller

Leonhard F. Feiner

Johannes Brandt

Daniel Rueckert

Georgios Kaissis

ArXiv (abs)PDF HTML

Papers citing "Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging"

15 / 15 papers shown

Title
Visual Privacy Auditing with Diffusion Models Kristian Schwethelm Johannes Kaiser Moritz Knolle Daniel Rueckert Daniel Rueckert Alexander Ziller DiffM AAML 103 0 0 12 Mar 2024
TMI! Finetuned Models Leak Private Information from their Pretraining Data John Abascal Stanley Wu Alina Oprea Jonathan R. Ullman 86 18 0 01 Jun 2023
TAN Without a Burn: Scaling Laws of DP-SGD Tom Sander Pierre Stock Alexandre Sablayrolles FedML 86 43 0 07 Oct 2022
Reconstructing Training Data from Trained Neural Networks Niv Haim Gal Vardi Gilad Yehudai Ohad Shamir Michal Irani 91 141 0 15 Jun 2022
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data Joceline Ziegler Bjarne Pfitzner H. Schulz A. Saalbach B. Arnrich FedML 59 17 0 06 May 2022
Differentially private training of residual networks with scale normalisation Helena Klause Alexander Ziller Daniel Rueckert Kerstin Hammernik Georgios Kaissis 67 34 0 01 Mar 2022
Defending against Reconstruction Attacks with Rényi Differential Privacy Pierre Stock I. Shilov Ilya Mironov Alexandre Sablayrolles AAML SILM MIACV 65 40 0 15 Feb 2022
Variational Model Inversion Attacks Kuan-Chieh Wang Yanzhe Fu Ke Li Ashish Khisti R. Zemel Alireza Makhzani MIACV 81 97 0 26 Jan 2022
Reconstructing Training Data with Informed Adversaries Borja Balle Giovanni Cherubin Jamie Hayes MIACV AAML 93 171 0 13 Jan 2022
When the Curious Abandon Honesty: Federated Learning Is Not Private Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML AAML 101 186 0 06 Dec 2021
The Medical Segmentation Decathlon Michela Antonelli Annika Reinke Spyridon Bakas Keyvan Farahani AnnetteKopp-Schneider ... Zhanwei Xu Yefeng Zheng Amber L. Simpson Lena Maier-Hein M. Jorge Cardoso OOD 121 990 0 10 Jun 2021
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 121 1,234 0 31 Mar 2020
Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan Vitaly Shmatikov 153 482 0 28 May 2019
Towards Formalizing the GDPR's Notion of Singling Out A. Cohen Kobbi Nissim 80 84 0 12 Apr 2019
A large annotated medical image dataset for the development and evaluation of segmentation algorithms Amber L. Simpson Michela Antonelli Spyridon Bakas Michel Bilello Keyvan Farahani ... M. McHugo S. Napel Eugene Vorontsov Lena Maier-Hein M. Jorge Cardoso 117 862 0 25 Feb 2019

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.