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Multiple Linear-Combination Security Network Coding

In this paper, we put forward the model of multiple linear-combination security multicast network coding, where the wiretapper desires to obtain some information about a predefined set of multiple linear combinations of the source symbols by eavesdropping any one (but not more than one) channel subs...

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Detalles Bibliográficos
Autores principales: Bai, Yang, Guang, Xuan, Yeung, Raymond W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453641/
https://www.ncbi.nlm.nih.gov/pubmed/37628165
http://dx.doi.org/10.3390/e25081135
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author Bai, Yang
Guang, Xuan
Yeung, Raymond W.
author_facet Bai, Yang
Guang, Xuan
Yeung, Raymond W.
author_sort Bai, Yang
collection PubMed
description In this paper, we put forward the model of multiple linear-combination security multicast network coding, where the wiretapper desires to obtain some information about a predefined set of multiple linear combinations of the source symbols by eavesdropping any one (but not more than one) channel subset up to a certain size r, referred to as the security level. For this model, the security capacity is defined as the maximum average number of source symbols that can be securely multicast to all sink nodes for one use of the network under the linear-combination security constraint. For any security level and any linear-combination security constraint, we fully characterize the security capacity in terms of the ratio of the rank of the linear-combination security constraint to the number of source symbols. Also, we develop a general construction of linear security network codes. Finally, we investigate the asymptotic behavior of the security capacity for a sequence of linear-combination security models and discuss the asymptotic optimality of our code construction.
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spelling pubmed-104536412023-08-26 Multiple Linear-Combination Security Network Coding Bai, Yang Guang, Xuan Yeung, Raymond W. Entropy (Basel) Article In this paper, we put forward the model of multiple linear-combination security multicast network coding, where the wiretapper desires to obtain some information about a predefined set of multiple linear combinations of the source symbols by eavesdropping any one (but not more than one) channel subset up to a certain size r, referred to as the security level. For this model, the security capacity is defined as the maximum average number of source symbols that can be securely multicast to all sink nodes for one use of the network under the linear-combination security constraint. For any security level and any linear-combination security constraint, we fully characterize the security capacity in terms of the ratio of the rank of the linear-combination security constraint to the number of source symbols. Also, we develop a general construction of linear security network codes. Finally, we investigate the asymptotic behavior of the security capacity for a sequence of linear-combination security models and discuss the asymptotic optimality of our code construction. MDPI 2023-07-28 /pmc/articles/PMC10453641/ /pubmed/37628165 http://dx.doi.org/10.3390/e25081135 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bai, Yang
Guang, Xuan
Yeung, Raymond W.
Multiple Linear-Combination Security Network Coding
title Multiple Linear-Combination Security Network Coding
title_full Multiple Linear-Combination Security Network Coding
title_fullStr Multiple Linear-Combination Security Network Coding
title_full_unstemmed Multiple Linear-Combination Security Network Coding
title_short Multiple Linear-Combination Security Network Coding
title_sort multiple linear-combination security network coding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453641/
https://www.ncbi.nlm.nih.gov/pubmed/37628165
http://dx.doi.org/10.3390/e25081135
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