Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning. - Georgios Kaissis; Alexander Ziller; Stefan Kolek; Anneliese Riess; Daniel Rueckert (NeurIPS, 2023)

On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models. - Deniz Daum; Richard Osuala; Anneliese Riess; Georgios Kaissis; Julia A. Schnabel; Maxime Di Folco (DGM4MICCAI@MICCAI, 2024)

Enhancing the Utility of Privacy-Preserving Cancer Classification Using Synthetic Data. - Richard Osuala; Daniel Lang 003; Anneliese Riess; Georgios Kaissis; Zuzanna Szafranowska; Grzegorz Skorupko; Oliver Díaz; Julia A. Schnabel; Karim Lekadir (Deep-Breath@MICCAI, 2024)

Complex-Valued Federated Learning with Differential Privacy and MRI Applications. - Anneliese Riess; Alexander Ziller; Stefan Kolek; Daniel Rueckert; Julia A. Schnabel; Georgios Kaissis (ISIC/iMIMIC/EARTH/DeCaF@MICCAI, 2024)

Bounding Reconstruction Attack Success of Adversaries Without Data Priors. - Alexander Ziller; Anneliese Riess; Kristian Schwethelm; Tamara T. Mueller; Daniel Rueckert; Georgios Kaissis (CoRR, 2024)