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Salvaging Merkle-Damgård for Practical Applications

  • Yevgeniy Dodis
  • Thomas Ristenpart
  • Thomas Shrimpton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5479)

Abstract

Many cryptographic applications of hash functions are analyzed in the random oracle model. Unfortunately, most concrete hash functions, including the SHA family, use the iterative (strengthened) Merkle-Damgård transform applied to a corresponding compression function. Moreover, it is well known that the resulting “structured” hash function cannot be generically used as a random oracle, even if the compression function is assumed to be ideal. This leaves a large disconnect between theory and practice: although no attack is known for many concrete applications utilizing existing (Merkle-Damgård based) hash functions, there is no security guarantee either, even by idealizing the compression function.

Motivated by this question, we initiate a rigorous and modular study of developing new notions of (still idealized) hash functions which would be (a) natural and elegant; (b) sufficient for arguing security of important applications; and (c) provably met by the (strengthened) Merkle-Damgård transform, applied to a “strong enough” compression function. In particular, we develop two such notions satisfying (a)-(c): a preimage aware function ensures that the attacker cannot produce a “useful” output of the function without already “knowing” the corresponding preimage, and a public-use random oracle, which is a random oracle that reveals to attackers messages queried by honest parties.

Keywords

Hash Function Block Cipher Random Oracle Compression Function Random Oracle Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yevgeniy Dodis
    • 1
  • Thomas Ristenpart
    • 2
  • Thomas Shrimpton
    • 3
  1. 1.Dept. of Computer ScienceNew York UniversityUSA
  2. 2.Dept. of Computer Science & EngineeringUniversity of California San DiegoUSA
  3. 3.Dept. of Computer Science, Portland State University Faculty of InformaticsUniversity of LuganoSwitzerland

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