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Dmitrii Usynin
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2020 – today
- 2024
- [c3]Dmitrii Usynin
, Daniel Rueckert
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training. EICC 2024: 179-185 - [i19]Jack K. Fitzsimons, Agustín Freitas Pasqualini, Robert Pisarczyk, Dmitrii Usynin:
Naturally Private Recommendations with Determinantal Point Processes. CoRR abs/2405.13677 (2024) - [i18]Felix Hsieh, Huy H. Nguyen, AprilPyone MaungMaung, Dmitrii Usynin, Isao Echizen:
Mitigating Backdoor Attacks using Activation-Guided Model Editing. CoRR abs/2407.07662 (2024) - 2023
- [j6]Tamara T. Mueller
Differentially Private Graph Neural Networks for Whole-Graph Classification. IEEE Trans. Pattern Anal. Mach. Intell. 45(6): 7308-7318 (2023) - [j5]Dmitrii Usynin
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks. ACM Trans. Priv. Secur. 26(3): 38:1-38:30 (2023) - [c2]Tomás Chobola
Membership Inference Attacks Against Semantic Segmentation Models. AISec@CCS 2023: 43-53 - [i17]Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis:
Leveraging gradient-derived metrics for data selection and valuation in differentially private training. CoRR abs/2305.02942 (2023) - [i16]Dmitrii Usynin, Moritz Knolle, Georgios Kaissis:
SoK: Memorisation in machine learning. CoRR abs/2311.03075 (2023) - 2022
- [j4]Georgios Kaissis
Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index. J. Priv. Confidentiality 12(1) (2022) - [j3]Dmitrii Usynin, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis:
Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning. Proc. Priv. Enhancing Technol. 2022(1): 274-290 (2022) - [c1]Dmitrii Usynin, Helena Klause, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis:
Can Collaborative Learning Be Private, Robust and Scalable? DeCaF/FAIR@MICCAI 2022: 37-46 - [i15]Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis:
Differentially Private Graph Classification with GNNs. CoRR abs/2202.02575 (2022) - [i14]Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis:
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks. CoRR abs/2203.00481 (2022) - [i13]Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis:
SoK: Differential Privacy on Graph-Structured Data. CoRR abs/2203.09205 (2022) - [i12]Dmitrii Usynin, Helena Klause, Daniel Rueckert, Georgios Kaissis:
Can collaborative learning be private, robust and scalable? CoRR abs/2205.02652 (2022) - [i11]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 abs/2211.10173 (2022) - [i10]Tomás Chobola, Dmitrii Usynin, Georgios Kaissis:
Membership Inference Attacks Against Semantic Segmentation Models. CoRR abs/2212.01082 (2022) - 2021
- [j2]Georgios Kaissis
End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3(6): 473-484 (2021) - [j1]Dmitrii Usynin
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. Nat. Mach. Intell. 3(9): 749-758 (2021) - [e1]Cristina Oyarzun Laura, M. Jorge Cardoso
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. Lecture Notes in Computer Science 12969, Springer 2021, ISBN 978-3-030-90873-7 [contents] - [i9]Alexander Ziller, Dmitrii Usynin
Differentially private federated deep learning for multi-site medical image segmentation. CoRR abs/2107.02586 (2021) - [i8]Alexander Ziller, Dmitrii Usynin
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation. CoRR abs/2107.04265 (2021) - [i7]Moritz Knolle, Alexander Ziller, Dmitrii Usynin
Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty. CoRR abs/2107.04296 (2021) - [i6]Georgios Kaissis, Moritz Knolle, Friederike Jungmann, Alexander Ziller, Dmitrii Usynin
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index. CoRR abs/2109.10528 (2021) - [i5]Dmitrii Usynin
An automatic differentiation system for the age of differential privacy. CoRR abs/2109.10573 (2021) - [i4]Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin
Partial sensitivity analysis in differential privacy. CoRR abs/2109.10582 (2021) - [i3]Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kerstin Hammernik, Daniel Rueckert, Georgios Kaissis:
Complex-valued deep learning with differential privacy. CoRR abs/2110.03478 (2021) - [i2]Dmitrii Usynin, Alexander Ziller, Daniel Rueckert, Jonathan Passerat-Palmbach, Georgios Kaissis:
Distributed Machine Learning and the Semblance of Trust. CoRR abs/2112.11040 (2021) - 2020
- [i1]Alexander Ziller
Privacy-preserving medical image analysis. CoRR abs/2012.06354 (2020)
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