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Anneliese Riess
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2020 – today
- 2024
- [c3]Deniz Daum, Richard Osuala
, Anneliese Riess
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models. DGM4MICCAI@MICCAI 2024: 139-149 - [c2]Anneliese Riess, Alexander Ziller, Stefan Kolek, Daniel Rueckert, Julia A. Schnabel, Georgios Kaissis
Complex-Valued Federated Learning with Differential Privacy and MRI Applications. ISIC/iMIMIC/EARTH/DeCaF@MICCAI 2024: 191-203 - [i3]Alexander Ziller, Anneliese Riess, Kristian Schwethelm, Tamara T. Mueller, Daniel Rueckert, Georgios Kaissis:
Bounding Reconstruction Attack Success of Adversaries Without Data Priors. CoRR abs/2402.12861 (2024) - [i2]Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Díaz, Julia A. Schnabel, Karim Lekadir:
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data. CoRR abs/2407.12669 (2024) - [i1]Deniz Daum, Richard Osuala, Anneliese Riess, Georgios Kaissis, Julia A. Schnabel, Maxime Di Folco:
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models. CoRR abs/2407.16405 (2024) - 2023
- [c1]Georgios Kaissis, Alexander Ziller, Stefan Kolek, Anneliese Riess, Daniel Rueckert:
Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning. NeurIPS 2023
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