Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ http://www.cs.utexas...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
https://doi.org/10.1109/focs.2...
Article . 2008 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object
Data sources: DBLP
versions View all 2 versions
addClaim

Network Extractor Protocols

Authors: Yael Tauman Kalai; Xin Li 0006; Anup Rao 0001; David Zuckerman;

Network Extractor Protocols

Abstract

We design efficient protocols for processors to extract private randomness over a network with Byzantine faults, when each processor has access to an independent weakly-random n-bit source of sufficient min-entropy.We give several such network extractor protocols in both the information theoretic and computational settings.For a computationally unbounded adversary, we construct protocols in both the synchronous and asynchronous settings.These network extractors imply efficient protocols for leader election (synchronous setting only) and Byzantine agreement which tolerate a linear fraction of faults,even when the min-entropy is only 2(log n) Omega(1).For larger min-entropy,in the synchronous setting the fraction of tolerable faults approaches the bounds in the perfect-randomness case.Our network extractors for a computationally bounded adversary work in the synchronous setting even when 99% of the parties are faulty, assuming trapdoor permutations exist. Further, assuming a strong variant of the Decisional Diffie-Hellman Assumption, we construct a network extractor in which all parties receive private randomness. This yields an efficient protocol for secure multi-party computation with imperfect randomness, when the number of parties is at least polylog (n) and where the parties only have access to an independent source with min-entropy nOmega(1).

  • BIP!
    Impact byBIP!
    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).
    19
    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
Found an issue? Give us feedback
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!
19
Average
Top 10%
Top 10%