Relative Generalized Rank Weight of Linear Codes and Its Applications to Network Coding

Jun Kurihara, Rytaro Yamashita Matsumoto, Tomohiko Uyematsu

Research output: Contribution to journalJournal articleResearchpeer-review

43 Citations (Scopus)

Abstract

By extending the notion of minimum rank distance, this paper introduces two new relative code parameters of a linear code C{script}1 of length n over a field extension F{double-struck}qm and its subcode C{script}2 {subset of with not equal to} C{script}1. One is called the relative dimension/intersection profile (RDIP), and the other is called the relative generalized rank weight (RGRW). We clarify their basic properties and the relation between the RGRW and the minimum rank distance. As applications of the RDIP and the RGRW, the security performance and the error correction capability of secure network coding, guaranteed independently of the underlying network code, are analyzed and clarified. We propose a construction of secure network coding scheme, and analyze its security performance and error correction capability as an example of applications of the RDIP and the RGRW. Silva and Kschischang showed the existence of a secure network coding in which no part of the secret message is revealed to the adversary even if any C{script}1-1 links are wiretapped, which is guaranteed over any underlying network code. However, the explicit construction of such a scheme remained an open problem. Our new construction is just one instance of secure network coding that solves this open problem.

Original languageEnglish
Article number7101826
JournalIEEE Transactions on Information Theory
Volume61
Issue number7
Pages (from-to)3912-3936
Number of pages25
ISSN0018-9448
DOIs
Publication statusPublished - 1 Jul 2015

Keywords

  • Network error correction
  • rank distance
  • relative dimension/intersection profile
  • relative generalized Hamming weight
  • relative generalized rank weight
  • secure network coding

Fingerprint

Dive into the research topics of 'Relative Generalized Rank Weight of Linear Codes and Its Applications to Network Coding'. Together they form a unique fingerprint.

Cite this