An Analytical Model for the Partial Intercept Probability in Sparse Linear Network Coding

The security of linear network coding schemes such as Random Linear Network Coding (RLNC) and Sparse Random Linear Network Coding (SRLNC) is an important performance metric. One of the security aspects of these coding schemes is the probability that a potential eavesdropper recovers a fraction of so...

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Veröffentlicht in:IEEE communications letters Jg. 24; H. 4; S. 725 - 728
Hauptverfasser: Sehat, Hadi, Pahlevani, Peyman
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Analytical models
Approximation algorithms
Coding
Computer simulation
Decoding
Mathematical analysis
Mathematical models
Network coding
physical layer security
probability of partial decoding
Receivers
Security
Security aspects
Sparse matrices
Sparse random network coding
Sparsity
ISSN:1089-7798, 1558-2558
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Zusammenfassung:The security of linear network coding schemes such as Random Linear Network Coding (RLNC) and Sparse Random Linear Network Coding (SRLNC) is an important performance metric. One of the security aspects of these coding schemes is the probability that a potential eavesdropper recovers a fraction of source packets. In this work, we consider a network consisted of a sender, a legitimate receiver and an eavesdropper, where the sender uses SRLNC to broadcast data. We propose an analytical approximation for the probability of decoding a fraction of source packets, i.e., the partial intercept probability, by the eavesdropper. Using this analytical model, we propose an algorithm for the maximum sparsity that satisfies a threshold on the number of the source packets decoded by the eavesdropper. Using simulation technique, We proved that the maximum sparsity found by this algorithm satisfies the aforementioned threshold.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2020.2969349