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Publications of Riccardo Taiello
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Riccardo Taiello.
Privacy-preserving machine learning for large-scale collaborative healthcare data analysis.
Theses,
Université Côte d'Azur,
September 2024.
Keyword(s): Security and privacy,
Privacy enhancing technologies,
Federate learning,
Sécurité et confidentialité,
Technologies de renforcement de la confidentialité,
Apprentissage fédéré.
[bibtex-entry]
Articles in journal, book chapters |
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Riccardo Taiello,
Melek Önen,
Francesco Capano,
Olivier Humbert,
and Marco Lorenzi.
Privacy preserving image registration.
Medical Image Analysis,
94,
May 2024.
[bibtex-entry]
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Riccardo Taiello,
Melek Önen,
Clémentine Gritti,
and Marco Lorenzi.
Let Them Drop: Scalable and Efficient Federated Learning Solutions Agnostic to Stragglers.
In ARES 2024 - 19th International Conference on Availability, Reliability and Security,
number 13 of ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security,
Vienna, Austria,
pages 1-12,
July 2024.
ACM.
Keyword(s): Attribute-Based Encryption,
Time-based Access Control,
Direct Revocation,
Internet of Things,
preserving protocols,
Security protocols,
Synchronous and Asynchronous Federated Learning,
Secure Aggregation,
Role authorities,
Time authority,
Sensors Actuator Proxy.
[bibtex-entry]
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Riccardo Taiello,
Melek Önen,
Olivier Humbert,
and Marco Lorenzi.
Privacy Preserving Image Registration.
In MICCAI 2022 - Medical Image Computing and Computer Assisted Intervention,
Singapore, Singapore,
September 2022.
Keyword(s): Image Registration,
Privacy enhancing technologies,
Trustworthiness.
[bibtex-entry]
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Francesco Cremonesi,
Marc Vesin,
Sergen Cansiz,
Yannick Bouillard,
Irene Balelli,
Lucia Innocenti,
Santiago Silva,
Samy-Safwan Ayed,
Riccardo Taiello,
Laetita Kameni,
Richard Vidal,
Fanny Orlhac,
Christophe Nioche,
Nathan Lapel,
Bastien Houis,
Romain Modzelewski,
Olivier Humbert,
Melek Önen,
and Marco Lorenzi.
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications.
Note: Working paper or preprint,
April 2023.
Keyword(s): Machine learning,
Biomedical Application,
Healthcare,
Federated Learning Framework.
[bibtex-entry]
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