Faster SeaSign signatures through improved rejection sampling

Thomas Decru, Lorenz Panny, Frederik Vercauteren

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

4 Citaten (Scopus)

Samenvatting

We speed up the isogeny-based “SeaSign” signature scheme recently proposed by De Feo and Galbraith. The core idea in SeaSign is to apply the “Fiat–Shamir with aborts” transform to the parallel repeated execution of an identification scheme based on CSIDH. We optimize this general transform by allowing the prover to not answer a limited number of said parallel executions, thereby lowering the overall probability of rejection. The performance improvement ranges between factors of approximately 4.4 and 65.7 for various instantiations of the scheme, at the expense of roughly doubling the signature sizes.

Originele taal-2Engels
TitelPost-Quantum Cryptography - 10th International Conference, PQCrypto 2019, Revised Selected Papers
RedacteurenJintai Ding, Rainer Steinwandt
Plaats van productieCham
UitgeverijSpringer
Pagina's271-285
Aantal pagina's15
ISBN van geprinte versie9783030255091
DOI's
StatusGepubliceerd - 14 jul 2019
Evenement10th International Conference on Post-Quantum Cryptography, PQCrypto 2019 - Chongquin, China
Duur: 8 mei 201910 mei 2019

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11505 LNCS
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

Congres

Congres10th International Conference on Post-Quantum Cryptography, PQCrypto 2019
LandChina
StadChongquin
Periode8/05/1910/05/19

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  • Citeer dit

    Decru, T., Panny, L., & Vercauteren, F. (2019). Faster SeaSign signatures through improved rejection sampling. In J. Ding, & R. Steinwandt (editors), Post-Quantum Cryptography - 10th International Conference, PQCrypto 2019, Revised Selected Papers (blz. 271-285). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11505 LNCS). Springer. https://doi.org/10.1007/978-3-030-25510-7_15