New ID-Based Signatures without Trusted PKG

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	Similar Articles Articles by Lingling Xu Articles by Mingqiang Wang

First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)

Adelaide, Australia

January 23-January 24

ISBN: 0-7695-3090-7

	ASCII Text	x
	Lingling Xu, Mingqiang Wang, "New ID-Based Signatures without Trusted PKG," International Workshop on Knowledge Discovery and Data Mining, pp. 589-593, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008.

	BibTex	x
	@article{ 10.1109/WKDD.2008.93, author = {Lingling Xu and Mingqiang Wang}, title = {New ID-Based Signatures without Trusted PKG}, journal ={International Workshop on Knowledge Discovery and Data Mining}, volume = {0}, year = {2008}, isbn = {0-7695-3090-7}, pages = {589-593}, doi = {http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.93}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - International Workshop on Knowledge Discovery and Data Mining TI - New ID-Based Signatures without Trusted PKG SN - 0-7695-3090-7 SP589 EP593 A1 - Lingling Xu, A1 - Mingqiang Wang, PY - 2008 VL - 0 JA - International Workshop on Knowledge Discovery and Data Mining ER -

Lingling Xu

Mingqiang Wang

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.93

In this paper, from bilinear pairings, we propose two ID-based signatures without trusted PKG, which solve the key escrow problem. In the schemes, the PKG is prevented forging a legal user's signature for its traceability. Under random oracle model, our schemes are both proved secure against existential forgery on adaptively chosen message and ID attack, assuming the Computational Diffie-Hellman Problem (CDHP) in Diffie-Hellman group is intractable. By comparison, our schemes have higher efficiency than the existing schemes.

Citation:

Lingling Xu, Mingqiang Wang, "New ID-Based Signatures without Trusted PKG," wkdd, pp.589-593, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008), 2008

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