no code implementations • 18 Nov 2022 • Tamara T. Mueller, Stefan Kolek, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Daniel Rueckert, Georgios Kaissis
Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database.
no code implementations • 8 Nov 2022 • Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann, Daniel Rueckert, Rickmer Braren, Georgios Kaissis
The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity.
no code implementations • 22 Sep 2021 • Georgios Kaissis, Moritz Knolle, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Daniel Rueckert
$\psi$ uniquely characterises the GM and its properties by encapsulating its two fundamental quantities: the sensitivity of the query and the magnitude of the noise perturbation.
1 code implementation • 22 Sep 2021 • Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Friederike Jungmann, Daniel Rueckert, Georgios Kaissis
However, while techniques such as individual R\'enyi DP (RDP) allow for granular, per-person privacy accounting, few works have investigated the impact of each input feature on the individual's privacy loss.
1 code implementation • 2 Sep 2020 • Moritz Knolle, Georgios Kaissis, Friederike Jungmann, Sebastian Ziegelmayer, Daniel Sasse, Marcus Makowski, Daniel Rueckert, Rickmer Braren
For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial.