Hash function generation by means of Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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