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First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)
An Efficient (t, n)-Threshold Multi-Secret Sharing Scheme
Adelaide, Australia
January 23-January 24
ISBN: 0-7695-3090-7
ASCII Text
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Shi Runhua, Huang Liusheng, Zhong Hong, "An Efficient (t, n)-Threshold Multi-Secret Sharing Scheme," International Workshop on Knowledge Discovery and Data Mining, pp. 580-583, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008.
 
 
BibTex
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@article{ 10.1109/WKDD.2008.126,
author = {Shi Runhua and Huang Liusheng and Zhong Hong},
title = {An Efficient (t, n)-Threshold Multi-Secret Sharing Scheme},
journal ={International Workshop on Knowledge Discovery and Data Mining},
volume = {0},
year = {2008},
isbn = {0-7695-3090-7},
pages = {580-583},
doi = {http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.126},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - International Workshop on Knowledge Discovery and Data Mining
TI - An Efficient (t, n)-Threshold Multi-Secret Sharing Scheme
SN - 0-7695-3090-7
SP580
EP583
A1 - Shi Runhua,
A1 - Huang Liusheng,
A1 - Zhong Hong,
PY - 2008
VL - 0
JA - International Workshop on Knowledge Discovery and Data Mining
ER -
 
An efficient (t, n) threshold multi-secret sharing scheme is proposed in this paper. This scheme has the same power as Yang et al.'s scheme, in which there are secrets to be shared among n participants according to (t, n)-threshold access structure. But it uses the fixed ( )th degree Lagrange interpolation polynomial whenever p ) 1 ( > p 1 t t p or t p > while Yang et al.' scheme employs the ( 1 t ), ( )th degree Lagrange interpolation polynomial when , 1 p t p t p > , respectively. Thus, the computing time and the storage cost of this scheme is less than that of Yang et al.'s scheme.
Citation:
Shi Runhua, Huang Liusheng, Zhong Hong, "An Efficient (t, n)-Threshold Multi-Secret Sharing Scheme," wkdd, pp.580-583, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008
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