Alexander Ziller

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 35

Top Topics

Diffusion Model

Differential Privacy

Differentially Private

Top Venues

Nat. Mach. Intell.

J. Priv. Confidentiality

Publications

Alexander Ziller, Anneliese Riess, Kristian Schwethelm, Tamara T. Mueller, Daniel Rueckert, Georgios Kaissis
Bounding Reconstruction Attack Success of Adversaries Without Data Priors. CoRR (2024)
Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Daniel Rueckert, Georgios Kaissis, Alexander Ziller
Visual Privacy Auditing with Diffusion Models. CoRR (2024)
Tamara T. Müller, Sophie Starck, Kyriaki-Margarita Bintsi, Alexander Ziller, Rickmer Braren, Georgios Kaissis, Daniel Rueckert
Are Population Graphs Really as Powerful as Believed? Trans. Mach. Learn. Res. 2024 (2024)
Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl, Marcus R. Makowski, Sven Nebelung, Rickmer Braren, Daniel Rueckert, Daniel Truhn, Georgios Kaissis
Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology. CoRR (2023)
Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann, Daniel Rueckert, Rickmer Braren, Georgios Kaissis
Exploiting Segmentation Labels and Representation Learning to Forecast Therapy Response of PDAC Patients. ISBI (2023)
Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios Kaissis, Daniel Rueckert
Body Fat Estimation from Surface Meshes using Graph Neural Networks. CoRR (2023)
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)
Alexander Ziller, Alp Güvenir, Ayhan Can Erdur, Tamara T. Mueller, Philip Müller, Friederike Jungmann, Johannes Brandt, Jan Peeken, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
Explainable 2D Vision Models for 3D Medical Data. CoRR (2023)
Moritz Knolle, Robert Dorfman, Alexander Ziller, Daniel Rueckert, Georgios Kaissis
Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD. CoRR (2023)
Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios Kaissis, Daniel Rueckert
Body Fat Estimation from Surface Meshes Using Graph Neural Networks. ShapeMI@MICCAI (2023)
Georgios Kaissis, Jamie Hayes, Alexander Ziller, Daniel Rueckert
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy. CoRR (2023)
Felix Meissen, Johannes Getzner, Alexander Ziller, Georgios Kaissis, Daniel Rueckert
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection. CoRR (2023)
Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging. CoRR (2023)
Alexander Ziller, Tamara T. Mueller, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
Privacy: An axiomatic approach. CoRR (2022)
Georgios Kaissis, Alexander Ziller, Stefan Kolek Martinez de Azagra, Daniel Rueckert
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens. CoRR (2022)
Nicolas W. Remerscheid, Alexander Ziller, Daniel Rueckert, Georgios Kaissis
SmoothNets: Optimizing CNN architecture design for differentially private deep learning. CoRR (2022)
Alexander Ziller, Tamara T. Mueller, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
Privacy: An Axiomatic Approach. Entropy 24 (5) (2022)
Georgios Kaissis, Moritz Knolle, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Daniel Rueckert
Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index. J. Priv. Confidentiality 12 (1) (2022)
Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann, Daniel Rueckert, Rickmer Braren, Georgios Kaissis
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients. CoRR (2022)
Tamara T. Mueller, Stefan Kolek, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Daniel Rueckert, Georgios Kaissis
How Do Input Attributes Impact the Privacy Loss in Differential Privacy? CoRR (2022)
Helena Klause, Alexander Ziller, Daniel Rueckert, Kerstin Hammernik, Georgios Kaissis
Differentially private training of residual networks with scale normalisation. CoRR (2022)
Dmitrii Usynin, Alexander Ziller, Marcus R. Makowski, Rickmer Braren, Daniel Rueckert, Ben Glocker, Georgios Kaissis, Jonathan Passerat-Palmbach
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. Nat. Mach. Intell. 3 (9) (2021)
Moritz Knolle, Dmitrii Usynin, Alexander Ziller, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis
NeuralDP Differentially private neural networks by design. CoRR (2021)
Georgios Kaissis, Moritz Knolle, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Daniel Rueckert
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index. CoRR (2021)
Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kritika Prakash, Andrew Trask, Rickmer Braren, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation. CoRR (2021)
Alexander Ziller, Dmitrii Usynin, Nicolas Remerscheid, Moritz Knolle, Marcus R. Makowski, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
Differentially private federated deep learning for multi-site medical image segmentation. CoRR (2021)
Dmitrii Usynin, Alexander Ziller, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis
Distributed Machine Learning and the Semblance of Trust. CoRR (2021)
Dmitrii Usynin, Alexander Ziller, Moritz Knolle, Daniel Rueckert, Georgios Kaissis
An automatic differentiation system for the age of differential privacy. CoRR (2021)
Moritz Knolle, Alexander Ziller, Dmitrii Usynin, Rickmer Braren, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis
Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty. CoRR (2021)
Georgios Kaissis, Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Lima, Jason Mancuso, Friederike Jungmann, Marc-Matthias Steinborn, Andreas Saleh, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren
End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3 (6) (2021)
Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kerstin Hammernik, Daniel Rueckert, Georgios Kaissis
Complex-valued deep learning with differential privacy. CoRR (2021)
Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Friederike Jungmann, Daniel Rueckert, Georgios Kaissis
Partial sensitivity analysis in differential privacy. CoRR (2021)
Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Da Lima Costa Junior, Jason Mancuso, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren, Georgios Kaissis
Privacy-preserving medical image analysis. CoRR (2020)
Alexander Ziller, Julius Hansjakob, Vitalii Rusinov, Daniel Zügner, Peter Vogel, Stephan Günnemann
Oktoberfest Food Dataset. CoRR (2019)
Alexander Felfernig, Michael Jeran, Thorsten Ruprechter, Alexander Ziller, Stefan Reiterer, Martin Stettinger
Learning games for configuration and diagnosis tasks. Configuration Workshop (2015)