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pro vyhledávání: '"Margraf, Marian"'
Autor:
Reaz, Khan, Ardoin, Thibaud, Muth, Lea, Margraf, Marian, Wunder, Gerhard, Kholghi, Mahsa, Jansen, Kai, Zenger, Christian, Schmidt, Julian, Köppe, Enrico, Utkovski, Zoran, Bjelakovic, Igor, Schmieder, Mathis, Dressel, Olaf
Ultra-Wideband (UWB) technology re-emerges as a groundbreaking ranging technology with its precise micro-location capabilities and robustness. This paper highlights the security dimensions of UWB technology, focusing in particular on the intricacies
Externí odkaz:
http://arxiv.org/abs/2408.13124
Akademický článek
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Due to the rapid increase of digitization within our society, digital identities gain more and more importance. Provided by the German eID solution, every citizen has the ability to identify himself against various governmental and private organizati
Externí odkaz:
http://arxiv.org/abs/1701.04013
Autor:
Klein, Hauke, Margraf, Marian
The celebrated Erdos, Faber and Lovasz conjecture may be stated as follows: Any linear hypergraph on v points has chromatic index at most v. We will introduce the linear intersection number of a graph, and use this number to give an alternative formu
Externí odkaz:
http://arxiv.org/abs/math/0305073
Publikováno v:
Cryptography (2410-387X); Dec2023, Vol. 7 Issue 4, p49, 26p
Autor:
Wisiol, Nils, Mühl, Christopher, Pirnay, Niklas, Nguyen, Phuong Ha, Margraf, Marian, Seifert, Jean-Pierre, Dijk, Marten, Rührmair, Ulrich
Publikováno v:
Transactions on Cryptographic Hardware and Embedded Systems, Vol 2020, Iss 3 (2020)
IACR Transactions on Cryptographic Hardware and Embedded Systems, 2020(3), 97-120
IACR Transactions on Cryptographic Hardware and Embedded Systems, 2020(3), 97-120
We demonstrate that the Interpose PUF proposed at CHES 2019, an Arbiter PUF-based design for so-called Strong Physical Unclonable Functions (PUFs), can be modeled by novel machine learning strategies up to very substantial sizes and complexities. Our
Publikováno v:
In Computer Fraud & Security 2010 2010(9):14-17
Autor:
Sörries, Peter, Müller-Birn, Claudia, Glinka, Katrin, Boenisch, Franziska, Margraf, Marian, Sayegh-Jodehl, Sabine, Rose, Matthias
The application of machine learning (ML) in the medical domain has recently received a lot of attention. However, the constantly growing need for data in such ML-based approaches raises many privacy concerns, particularly when data originate from vul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c35e0be7a51a73e24a6e566deff4512