Privacy-preserving medical image analysis. - 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 (CoRR, 2020)

Differentially private federated deep learning for multi-site medical image segmentation. - Alexander Ziller; Dmitrii Usynin; Nicolas Remerscheid; Moritz Knolle; Marcus R. Makowski; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation. - Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Kritika Prakash; Andrew Trask; Rickmer Braren; Marcus R. Makowski; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty. - Moritz Knolle; Alexander Ziller; Dmitrii Usynin; Rickmer Braren; Marcus R. Makowski; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

An automatic differentiation system for the age of differential privacy. - Dmitrii Usynin; Alexander Ziller; Moritz Knolle; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Partial sensitivity analysis in differential privacy. - Tamara T. Mueller; Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Friederike Jungmann; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Complex-valued deep learning with differential privacy. - Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Kerstin Hammernik; Daniel Rueckert; Georgios Kaissis (CoRR, 2021)

Distributed Machine Learning and the Semblance of Trust. - Dmitrii Usynin; Alexander Ziller; Daniel Rueckert; Jonathan Passerat-Palmbach; Georgios Kaissis (CoRR, 2021)

End-to-end privacy preserving deep learning on multi-institutional medical imaging. - 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; et al. (Nat. Mach. Intell., 2021)

Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. - Dmitrii Usynin; Alexander Ziller; Marcus R. Makowski; Rickmer Braren; Daniel Rueckert; Ben Glocker; Georgios Kaissis; Jonathan Passerat-Palmbach (Nat. Mach. Intell., 2021)

Can Collaborative Learning Be Private, Robust and Scalable? - Dmitrii Usynin; Helena Klause; Johannes C. Paetzold; Daniel Rueckert; Georgios Kaissis (DeCaF/FAIR@MICCAI, 2022)

Differentially Private Graph Classification with GNNs. - Tamara T. Mueller; Johannes C. Paetzold; Chinmay Prabhakar; Dmitrii Usynin; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

SoK: Differential Privacy on Graph-Structured Data. - Tamara T. Mueller; Dmitrii Usynin; Johannes C. Paetzold; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

Can collaborative learning be private, robust and scalable? - Dmitrii Usynin; Helena Klause; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

How Do Input Attributes Impact the Privacy Loss in Differential Privacy? - Tamara T. Mueller; Stefan Kolek; Friederike Jungmann; Alexander Ziller; Dmitrii Usynin; Moritz Knolle; Daniel Rueckert; Georgios Kaissis (CoRR, 2022)

Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index. - Georgios Kaissis; Moritz Knolle; Friederike Jungmann; Alexander Ziller; Dmitrii Usynin; Daniel Rueckert (J. Priv. Confidentiality, 2022)

Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning. - Dmitrii Usynin; Daniel Rueckert; Jonathan Passerat-Palmbach; Georgios Kaissis (Proc. Priv. Enhancing Technol., 2022)

Membership Inference Attacks Against Semantic Segmentation Models. - Tomás Chobola; Dmitrii Usynin; Georgios Kaissis (AISec@CCS, 2023)

Leveraging gradient-derived metrics for data selection and valuation in differentially private training. - Dmitrii Usynin; Daniel Rueckert; Georgios Kaissis (CoRR, 2023)

SoK: Memorisation in machine learning. - Dmitrii Usynin; Moritz Knolle; Georgios Kaissis (CoRR, 2023)

Differentially Private Graph Neural Networks for Whole-Graph Classification. - Tamara T. Mueller; Johannes C. Paetzold; Chinmay Prabhakar; Dmitrii Usynin; Daniel Rueckert; Georgios Kaissis (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks. - Dmitrii Usynin; Daniel Rueckert; Georgios Kaissis (ACM Trans. Priv. Secur., 2023)

Mitigating Backdoor Attacks Using Activation-Guided Model Editing. - Felix Hsieh; Huy H. Nguyen; AprilPyone MaungMaung; Dmitrii Usynin; Isao Echizen (ACCV Workshops (1), 2024)

Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training. - Dmitrii Usynin; Daniel Rueckert; Georgios Kaissis (EICC, 2024)

Naturally Private Recommendations with Determinantal Point Processes. - Jack K. Fitzsimons; Agustín Freitas Pasqualini; Robert Pisarczyk; Dmitrii Usynin (CoRR, 2024)

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models. - Olivia Ma; Jonathan Passerat-Palmbach; Dmitrii Usynin (CoRR, 2024)

Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results. - Tamara T. Mueller; Dmitrii Usynin; Johannes C. Paetzold; Rickmer Braren; Daniel Rueckert; Georgios Kaissis (J. Priv. Confidentiality, 2024)

Memorisation in Machine Learning: A Survey of Results. - Dmitrii Usynin; Moritz Knolle; Georgios Kaissis (Trans. Mach. Learn. Res., 2024)