Dmitrii Usynin

36 papers Journal 31Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A LINDDUN-based Privacy Threat Modeling Framework for GenAI. CoRR Qianying Liao, Jonah Bellemans, Laurens Sion, Xue Jiang, Dmitrii Usynin, Xuebing Zhou, Dimitri Van Landuyt, Lieven Desmet, Wouter Joosen
2026	J	`jnl`	One stout to rule them all: Reconciling artificial intelligence, data science and malted alcoholic beverages. CoRR Dmitrii Usynin, Elena Shmakova, Michael Rheinberger
2025	J	`jnl`	Biologically-Informed Hybrid Membership Inference Attacks on Generative Genomic Models. CoRR Asia Belfiore, Jonathan Passerat-Palmbach, Dmitrii Usynin
2024	J	`jnl`	Differentially Private Guarantees for Analytics and Machine Learning on Graphs: A Survey of Results. J. Priv. Confidentiality Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
2024	J	`jnl`	Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models. CoRR Olivia Ma, Jonathan Passerat-Palmbach, Dmitrii Usynin
2024	—	`conf`	Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training. EICC Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
2024	J	`jnl`	Memorisation in Machine Learning: A Survey of Results. Trans. Mach. Learn. Res. Dmitrii Usynin, Moritz Knolle, Georgios Kaissis
2024	—	`conf`	Mitigating Backdoor Attacks Using Activation-Guided Model Editing. ACCV Workshops (1) Felix Hsieh, Huy H. Nguyen, AprilPyone MaungMaung, Dmitrii Usynin, Isao Echizen
2024	J	`jnl`	Mitigating Backdoor Attacks using Activation-Guided Model Editing. CoRR Felix Hsieh, Huy H. Nguyen, AprilPyone MaungMaung, Dmitrii Usynin, Isao Echizen
2024	J	`jnl`	Naturally Private Recommendations with Determinantal Point Processes. CoRR Jack K. Fitzsimons, Agustín Freitas Pasqualini, Robert Pisarczyk, Dmitrii Usynin
2023	J	`jnl`	Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks. ACM Trans. Priv. Secur. Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
2023	J	`jnl`	Differentially Private Graph Neural Networks for Whole-Graph Classification. IEEE Trans. Pattern Anal. Mach. Intell. Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
2023	J	`jnl`	Leveraging gradient-derived metrics for data selection and valuation in differentially private training. CoRR Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
2023	—	`conf`	Membership Inference Attacks Against Semantic Segmentation Models. AISec@CCS Tomás Chobola, Dmitrii Usynin, Georgios Kaissis
2023	J	`jnl`	SoK: Memorisation in machine learning. CoRR Dmitrii Usynin, Moritz Knolle, Georgios Kaissis
2022	J	`jnl`	Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks. CoRR Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
2022	—	`conf`	Can Collaborative Learning Be Private, Robust and Scalable? DeCaF/FAIR@MICCAI Dmitrii Usynin, Helena Klause, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis
2022	J	`jnl`	Can collaborative learning be private, robust and scalable? CoRR Dmitrii Usynin, Helena Klause, Daniel Rueckert, Georgios Kaissis
2022	J	`jnl`	Differentially Private Graph Classification with GNNs. CoRR Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
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`	Membership Inference Attacks Against Semantic Segmentation Models. CoRR Tomás Chobola, Dmitrii Usynin, Georgios Kaissis
2022	J	`jnl`	SoK: Differential Privacy on Graph-Structured Data. CoRR Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, 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
2022	J	`jnl`	Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning. Proc. Priv. Enhancing Technol. Dmitrii Usynin, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis
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