The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Une chaîne de hachage H pour une fonction de hachage unidirectionnelle h(·) est une séquence de valeurs de hachage v0, v1, ..., vn >, où vn est une valeur secrète, vi est généré par vi = h(vi+1) Pour i = n-1, n-2, ..., 0 et v0 est une valeur publique. Un algorithme de traversée de chaîne de hachage T calcule et génère la chaîne de hachage H, retour vi dans une période de temps (appelée ronde) i pour 1 ≤ i ≤ n. Au début, T des magasins soigneusement choisis κ valeurs de hachage (y compris vn) de H in κ stockages de mémoire (appelés cailloux). En rond i, T effectue deux types de calculs ; calcul en ligne vers la sortie vi avec des valeurs de hachage stockées dans des cailloux, puis un calcul préparatoire pour réorganiser les cailloux pour les tours futurs. Habituellement, le calcul en ligne consiste en une évaluation de fonction de hachage ou en une évaluation nulle, tandis que le calcul préparatoire occupe la majeure partie du coût de calcul. L’objectif de conception des algorithmes de traversée de chaîne de hachage précédents était de minimiser le coût de calcul par tour dans le pire des cas avec un minimum de cailloux. Au contraire, nous étudions un problème d’optimisation différent consistant à minimiser le coût de calcul moyen d’un cas. L'algorithme de traversée proposé réduit le coût de calcul moyen d'un cas de 20 à 30 % et le coût de calcul en ligne de 23 à 33 % pour les paramètres d'intérêt pratique. Par exemple, si l'algorithme proposé est implémenté sur des appareils alimentés par batterie, la durée de vie de la batterie peut être augmentée de 20 à 30 %.
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Dae Hyun YUM, Jae Woo SEO, Pil Joong LEE, "Energy-Efficient Hash Chain Traversal" in IEICE TRANSACTIONS on Fundamentals,
vol. E94-A, no. 3, pp. 955-963, March 2011, doi: 10.1587/transfun.E94.A.955.
Abstract: A hash chain H for a one-way hash function h(·) is a sequence of hash values < v0, v1, ..., vn >, where vn is a secret value, vi is generated by vi = h(vi+1) for i = n-1, n-2, ..., 0 and v0 is a public value. A hash chain traversal algorithm T computes and outputs the hash chain H, returning vi in time period (called round) i for 1 ≤ i ≤ n. At the outset, T stores carefully chosen κ hash values (including vn) of H in κ memory storages (called pebbles). In round i, T performs two kinds of computations; online computation to output vi with hash values stored in pebbles and then preparatory computation to rearrange pebbles for future rounds. Usually, the online computation consists of either one or zero hash function evaluation, while the preparatory computation occupies most of the computational cost. The design goal of previous hash chain traversal algorithms was to minimize the worst case computational cost per round with minimal pebbles. On the contrary, we study a different optimization problem of minimizing the average case computational cost. Our proposed traversal algorithm reduces the average case computational cost by 20-30% and the online computational cost by 23-33% for parameters of practical interest. For example, if the proposed algorithm is implemented on battery-powered devices, the battery lifetime can be increased by 20-30%.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.955/_p
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@ARTICLE{e94-a_3_955,
author={Dae Hyun YUM, Jae Woo SEO, Pil Joong LEE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Energy-Efficient Hash Chain Traversal},
year={2011},
volume={E94-A},
number={3},
pages={955-963},
abstract={A hash chain H for a one-way hash function h(·) is a sequence of hash values < v0, v1, ..., vn >, where vn is a secret value, vi is generated by vi = h(vi+1) for i = n-1, n-2, ..., 0 and v0 is a public value. A hash chain traversal algorithm T computes and outputs the hash chain H, returning vi in time period (called round) i for 1 ≤ i ≤ n. At the outset, T stores carefully chosen κ hash values (including vn) of H in κ memory storages (called pebbles). In round i, T performs two kinds of computations; online computation to output vi with hash values stored in pebbles and then preparatory computation to rearrange pebbles for future rounds. Usually, the online computation consists of either one or zero hash function evaluation, while the preparatory computation occupies most of the computational cost. The design goal of previous hash chain traversal algorithms was to minimize the worst case computational cost per round with minimal pebbles. On the contrary, we study a different optimization problem of minimizing the average case computational cost. Our proposed traversal algorithm reduces the average case computational cost by 20-30% and the online computational cost by 23-33% for parameters of practical interest. For example, if the proposed algorithm is implemented on battery-powered devices, the battery lifetime can be increased by 20-30%.},
keywords={},
doi={10.1587/transfun.E94.A.955},
ISSN={1745-1337},
month={March},}
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TY - JOUR
TI - Energy-Efficient Hash Chain Traversal
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 955
EP - 963
AU - Dae Hyun YUM
AU - Jae Woo SEO
AU - Pil Joong LEE
PY - 2011
DO - 10.1587/transfun.E94.A.955
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2011
AB - A hash chain H for a one-way hash function h(·) is a sequence of hash values < v0, v1, ..., vn >, where vn is a secret value, vi is generated by vi = h(vi+1) for i = n-1, n-2, ..., 0 and v0 is a public value. A hash chain traversal algorithm T computes and outputs the hash chain H, returning vi in time period (called round) i for 1 ≤ i ≤ n. At the outset, T stores carefully chosen κ hash values (including vn) of H in κ memory storages (called pebbles). In round i, T performs two kinds of computations; online computation to output vi with hash values stored in pebbles and then preparatory computation to rearrange pebbles for future rounds. Usually, the online computation consists of either one or zero hash function evaluation, while the preparatory computation occupies most of the computational cost. The design goal of previous hash chain traversal algorithms was to minimize the worst case computational cost per round with minimal pebbles. On the contrary, we study a different optimization problem of minimizing the average case computational cost. Our proposed traversal algorithm reduces the average case computational cost by 20-30% and the online computational cost by 23-33% for parameters of practical interest. For example, if the proposed algorithm is implemented on battery-powered devices, the battery lifetime can be increased by 20-30%.
ER -