Alexander Ziller

43 papers A* 1Journal 36Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	An Exploratory Survey Study Toward a Conceptual Framework for Structuring the Integration of Privacy-Enhancing Technologies in Federated Learning and Analytics. IEEE Access Hammam Abu Attieh, Mehmed Halilovic, Raouf Kerkouche, Shadi Rahimian, Hui-Po Wang, Raphael Rehms, Georgios Kaissis, Alexander Ziller, Jonas Kuntzer, Karen Otte, Thierry Meurers, Mario Fritz, Daniel Rückert, Fabian Prasser
2026	J	`jnl`	Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy. CoRR Johannes Kaiser, Alexander Ziller, Eleni Triantafillou, Daniel Rückert, Georgios Kaissis
2025	J	`jnl`	On Arbitrary Predictions from Equally Valid Models. CoRR Sarah Lockfisch, Kristian Schwethelm, Martin J. Menten, Rickmer Braren, Daniel Rueckert, Alexander Ziller, Georgios Kaissis
2025	J	`jnl`	Sensitivity, Specificity, and Consistency: A Tripartite Evaluation of Privacy Filters for Synthetic Data Generation. CoRR Adil Koeken, Alexander Ziller, Moritz Knolle, Daniel Rueckert
2025	J	`jnl`	Visual Privacy Auditing with Diffusion Models. Trans. Mach. Learn. Res. Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Sarah Lockfisch, Daniel Rueckert, Alexander Ziller
2024	J	`jnl`	Are Population Graphs Really as Powerful as Believed? Trans. Mach. Learn. Res. Tamara T. Müller, Sophie Starck, Kyriaki-Margarita Bintsi, Alexander Ziller, Rickmer Braren, Georgios Kaissis, Daniel Rueckert
2024	J	`jnl`	Bounding Reconstruction Attack Success of Adversaries Without Data Priors. CoRR Alexander Ziller, Anneliese Riess, Kristian Schwethelm, Tamara T. Mueller, Daniel Rueckert, Georgios Kaissis
2024	—	`conf`	Complex-Valued Federated Learning with Differential Privacy and MRI Applications. ISIC/iMIMIC/EARTH/DeCaF@MICCAI Anneliese Riess, Alexander Ziller, Stefan Kolek, Daniel Rueckert, Julia A. Schnabel, Georgios Kaissis
2024	—	`conf`	Fair and Private CT Contrast Agent Detection. FAIMI/EPIMI@MICCAI Philipp Kaess, Alexander Ziller, Lea Mantz, Daniel Rueckert, Florian J. Fintelmann, Georgios Kaissis
2024	J	`jnl`	Reconciling privacy and accuracy in AI for medical imaging. Nat. Mac. Intell. Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2024	J	`jnl`	Visual Privacy Auditing with Diffusion Models. CoRR Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Daniel Rueckert, Georgios Kaissis, Alexander Ziller
2023	J	`jnl`	Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD. CoRR Moritz Knolle, Robert Dorfman, Alexander Ziller, Daniel Rueckert, Georgios Kaissis
2023	—	`conf`	Body Fat Estimation from Surface Meshes Using Graph Neural Networks. ShapeMI@MICCAI Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios Kaissis, Daniel Rueckert
2023	J	`jnl`	Body Fat Estimation from Surface Meshes using Graph Neural Networks. CoRR Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios Kaissis, Daniel Rueckert
2023	J	`jnl`	Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy. CoRR Georgios Kaissis, Jamie Hayes, Alexander Ziller, Daniel Rueckert
2023	J	`jnl`	Explainable 2D Vision Models for 3D Medical Data. CoRR Alexander Ziller, Alp Güvenir, Ayhan Can Erdur, Tamara T. Mueller, Philip Müller, Friederike Jungmann, Johannes Brandt, Jan Peeken, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2023	—	`conf`	Exploiting Segmentation Labels and Representation Learning to Forecast Therapy Response of PDAC Patients. ISBI Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann, Daniel Rueckert, Rickmer Braren, Georgios Kaissis
2023	J	`jnl`	How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection. CoRR Felix Meissen, Johannes Getzner, Alexander Ziller, Georgios Kaissis, Daniel Rueckert
2023	A*	`conf`	Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning. NeurIPS Georgios Kaissis, Alexander Ziller, Stefan Kolek, Anneliese Riess, Daniel Rueckert
2023	J	`jnl`	Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology. CoRR Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl, Marcus R. Makowski, Sven Nebelung, Rickmer Braren, Daniel Rueckert, Daniel Truhn, Georgios Kaissis
2023	J	`jnl`	Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging. CoRR Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2022	J	`jnl`	Differentially private training of residual networks with scale normalisation. CoRR Helena Klause, Alexander Ziller, Daniel Rueckert, Kerstin Hammernik, Georgios Kaissis
2022	J	`jnl`	Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients. CoRR Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann, Daniel Rueckert, Rickmer Braren, Georgios Kaissis
2022	J	`jnl`	Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens. CoRR Georgios Kaissis, Alexander Ziller, Stefan Kolek Martinez de Azagra, Daniel Rueckert
2022	J	`jnl`	How Do Input Attributes Impact the Privacy Loss in Differential Privacy? CoRR Tamara T. Mueller, Stefan Kolek, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Daniel Rueckert, Georgios Kaissis
2022	J	`jnl`	Privacy: An Axiomatic Approach. Entropy Alexander Ziller, Tamara T. Mueller, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2022	J	`jnl`	Privacy: An axiomatic approach. CoRR Alexander Ziller, Tamara T. Mueller, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2022	J	`jnl`	SmoothNets: Optimizing CNN architecture design for differentially private deep learning. CoRR Nicolas W. Remerscheid, Alexander Ziller, Daniel Rueckert, Georgios Kaissis
2022	J	`jnl`	Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index. J. Priv. Confidentiality Georgios Kaissis, Moritz Knolle, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Daniel Rueckert
2021	J	`jnl`	A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index. CoRR Georgios Kaissis, Moritz Knolle, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin, Daniel Rueckert
2021	J	`jnl`	Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. Nat. Mach. Intell. Dmitrii Usynin, Alexander Ziller, Marcus R. Makowski, Rickmer Braren, Daniel Rueckert, Ben Glocker, Georgios Kaissis, Jonathan Passerat-Palmbach
2021	J	`jnl`	An automatic differentiation system for the age of differential privacy. CoRR Dmitrii Usynin, Alexander Ziller, Moritz Knolle, Daniel Rueckert, Georgios Kaissis
2021	—	`ed.`	Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning - 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings CLIP/DCL/LL-COVID19/PPML@MICCAI Cristina Oyarzun Laura, M. Jorge Cardoso, Michal Rosen-Zvi, Georgios Kaissis, Marius George Linguraru, Raj Shekhar, Stefan Wesarg, Marius Erdt, Klaus Drechsler, Yufei Chen, Shadi Albarqouni, Spyridon Bakas, Bennett A. Landman, Nicola Rieke, Holger Roth, Xiaoxiao Li, Daguang Xu, Maria Gabrani, Ender Konukoglu, Michal Guindy, Daniel Rueckert, Alexander Ziller, Dmitrii Usynin, Jonathan Passerat-Palmbach
2021	J	`jnl`	Complex-valued deep learning with differential privacy. CoRR Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kerstin Hammernik, Daniel Rueckert, Georgios Kaissis
2021	J	`jnl`	Differentially private federated deep learning for multi-site medical image segmentation. CoRR Alexander Ziller, Dmitrii Usynin, Nicolas Remerscheid, Moritz Knolle, Marcus R. Makowski, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2021	J	`jnl`	Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty. CoRR Moritz Knolle, Alexander Ziller, Dmitrii Usynin, Rickmer Braren, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis
2021	J	`jnl`	Distributed Machine Learning and the Semblance of Trust. CoRR Dmitrii Usynin, Alexander Ziller, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis
2021	J	`jnl`	End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 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
2021	J	`jnl`	Partial sensitivity analysis in differential privacy. CoRR Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Friederike Jungmann, Daniel Rueckert, Georgios Kaissis
2021	J	`jnl`	Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation. CoRR Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kritika Prakash, Andrew Trask, Rickmer Braren, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis
2020	J	`jnl`	Privacy-preserving medical image analysis. CoRR 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
2019	J	`jnl`	Oktoberfest Food Dataset. CoRR Alexander Ziller, Julius Hansjakob, Vitalii Rusinov, Daniel Zügner, Peter Vogel, Stephan Günnemann
2015	—	`conf`	Learning games for configuration and diagnosis tasks. Configuration Workshop Alexander Felfernig, Michael Jeran, Thorsten Ruprechter, Alexander Ziller, Stefan Reiterer, Martin Stettinger