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Cited by 216

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Secure Network Coding on a Wiretap Network

Cai, Ning, Yeung, Raymond W.·2011·IEEE Transactions on Information Theory

Abstract

In the paradigm of network coding, the nodes in a network are allowed to encode the information received from the input links. With network coding, the full capacity of the network can be utilized. In this paper, we propose a model, call the wiretap network, that incorporates information security with network coding. In this model, a collection of subsets of the channels in the network is given, and a wiretapper is allowed to access any one (but not more than one) of these subsets without being able to obtain any information about the message transmitted. Our model includes secret sharing in classical cryptography as a special case. We present a construction of secure linear network codes that can be used provided a certain graph-theoretic condition is satisfied. We also prove the necessity of this condition for the special case that the wiretapper may choose to access any subset of channels of a fixed size. The optimality of our code construction is established for this special case. Finally, we extend our results to the scenario when the wiretapper is allowed to obtain a controlled amount of information about the message.

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Metadata

Authors
Cai, N., & Yeung, R. W.
Venue
IEEE Transactions on Information Theory
Published
2011-01
Volume
57
Issue
1
Pages
424-435
ISSN
  • 0018-9448 (ISSN-L)
  • 1557-9654
OpenAlex
W2006947308
Cited by
216
References
3
FWCI
13.26
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Secure Network Coding on a Wiretap Network. https://app.cochat.ai/papers/10.1109/tit.2010.2090197
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Cai, N., & Yeung, R. W. (2011). Secure Network Coding on a Wiretap Network. IEEE Transactions on Information Theory. https://doi.org/10.1109/tit.2010.2090197

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.