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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ACM Transactions on ...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
DBLP
Article . 2008
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Logarithmic keying

Authors: Ehab S. Elmallah; Mohamed G. Gouda; Sandeep S. Kulkarni;

Logarithmic keying

Abstract

Consider a communication network where each process needs to securely exchange messages with its neighboring processes. In this network, each sent message is encrypted using one or more symmetric keys that are shared only between two processes: the process that sends the message and the neighboring process that receives the message. A straightforward scheme for assigning symmetric keys to the different processes in such a network is to assign each process O ( d ) keys, where d is the maximum number of neighbors of any process in the network. In this article, we present a more efficient scheme for assigning symmetric keys to the different processes in a communication network. This scheme, which is referred to as logarithmic keying, assigns O (log d ) symmetric keys to each process in the network. We show that logarithmic keying can be used in rich classes of communication networks that include star networks, acyclic networks, limited-cycle networks, planar networks, and dense bipartite networks. In addition, we present a construction that utilizes efficient keying schemes for general bipartite networks to construct efficient keying schemes for general networks.

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Top 10%
Top 10%
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