Concept
Confidentialité
Publications associées (19)
Privacy-preserving federated neural network training and inference
Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...
EPFL2023CALYPSO: Private Data Management for Decentralized Ledgers
Bryan Alexander Ford, Linus Gasser, Eleftherios Kokoris Kogias, Philipp Svetolik Jovanovic, Enis Ceyhun Alp
Distributed ledgers provide high availability and integrity, making them a key enabler for practical and secure computation of distributed workloads among mutually distrustful parties. Many practical applications also require strong confidentiality, howeve ...
2021Balancing hyperbole and impact in research communications related to lead-free piezoelectric materials (Retraction of Vol 54, Pg 11759, 2019)
This article has been retracted due to a breach in confidentiality. The confidential information has been redacted to protect the privacy of those involved. The authors have been invited to re-submit a modified version of their manuscript for publication. ...
SPRINGER2020In-situ experimental benchmarking of solid oxide fuel cell metal interconnect solutions
Jan Van Herle, Stefan Diethelm, Priscilla Caliandro, Manuel Bianco
The progress in the diffusion of solid oxide fuel cell (SOFC) as commercial devices is not paired by literature production. Articles describing the behaviour of SOFC stacks are rare because of confidentiality reasons for commercial suppliers while research ...
2020Time in cryptography
Time travel has always been a fascinating topic in literature and physics. In cryptography, one may wonder how to keep data confidential for some time. In this dissertation, we will study how to make private information travel to the future. This dissertat ...
EPFL2020