Hash function generation by means of Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{journals/umcs/VarretteMB12,
  author =       "Sebastien Varrette and Jakub Muszynski and 
                 Pascal Bouvry",
  title =        "Hash function generation by means of Gene Expression
                 Programming",
  journal =      "Annales UMCS, Informatica",
  year =         "2012",
  number =       "3",
  volume =       "12",
  pages =        "37--53",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  bibdate =      "2013-12-16",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/umcs/umcs12.html#VarretteMB12",
  ISSN =         "1732-1360",
  URL =          "http://dx.doi.org/10.2478/v10065-012-0027-x",
  DOI =          "doi:10.2478/v10065-012-0027-x",
  size =         "17 pages",
  abstract =     "Cryptographic hash functions are fundamental
                 primitives in modern cryptography and have many
                 security applications (data integrity checking,
                 cryptographic protocols, digital signatures, pseudo
                 random number generators etc.). At the same time novel
                 hash functions are designed (for instance in the
                 framework of the SHA-3 contest organized by the
                 National Institute of Standards and Technology (NIST)),
                 the cryptanalysts exhibit a set of statistical metrics
                 (propagation criterion, frequency analysis etc.) able
                 to assert the quality of new proposals. Also, rules to
                 design {"}good{"} hash functions are now known and are
                 followed in every reasonable proposal of a new hash
                 scheme. This article investigates the ways to build on
                 this experiment and those metrics to generate
                 automatically compression functions by means of
                 Evolutionary Algorithms (EAs). Such functions are at
                 the heart of the construction of iterative hash schemes
                 and it is therefore crucial for them to hold good
                 properties. Actually, the idea to use nature-inspired
                 heuristics for the design of such cryptographic
                 primitives is not new: this approach has been
                 successfully applied in several previous works,
                 typically using the Genetic Programming (GP) heuristic
                 [1]. Here, we exploit a hybrid meta-heuristic for the
                 evolutionary process called Gene Expression Programming
                 (GEP) [2] that appeared far more efficient
                 computationally speaking compared to the GP paradigm
                 used in the previous papers. In this context, the
                 GEPHashSearch framework is presented. As it is still a
                 work in progress, this article focuses on the design
                 aspects of this framework (individuals definitions,
                 fitness objectives etc.) rather than on complete
                 implementation details and validation results. Note
                 that we propose to tackle the generation of compression
                 functions as a multi-objective optimization problem in
                 order to identify the Pareto front i.e. the set of
                 non-dominated functions over the four fitness criteria
                 considered. If this goal is not yet reached, the first
                 experimental results in a mono-objective context are
                 promising and open the perspective of fruitful
                 contributions to the cryptographic community",
}

Genetic Programming entries for Sebastien Varrette Jakub Muszynski Pascal Bouvry

Citations