Efficient Construction of the Kite Generator Revisited

Orr Dunkelman, Ariel Weizman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The kite generator, first introduced by Andreeva et al.[1], is a strongly connected directed graph that allows creating a message of almost any desired length, connecting two chaining values covered by the kite generator. The kite generator can be used in second pre-image attacks against (dithered) Merkle-Damgård hash functions. In this work we discuss the complexity of constructing the kite generator. We show that the analysis of the construction of the kite generator first described by Andreeva et al.is somewhat inaccurate and discuss its actual complexity. We follow with presenting a new method for a more efficient construction of the kite generator, cutting the running time of the preprocessing by half (compared with the original claims of Andreeva et al. or by a linear factor compared to corrected analysis). Finally, we adapt the new method to the dithered Merkle-Damgård structure.

Original languageEnglish
Title of host publicationCyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings
EditorsItai Dinur, Shlomi Dolev, Sachin Lodha
PublisherSpringer Verlag
Pages6-19
Number of pages14
ISBN (Print)9783319941462
DOIs
StatePublished - 2018
Event2nd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018 - Beer-Sheva, Israel
Duration: 21 Jun 201822 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10879 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018
Country/TerritoryIsrael
CityBeer-Sheva
Period21/06/1822/06/18

Bibliographical note

Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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