Article

Evolving hash functions by means of genetic programming

Authors:

César Estébanez,

Julio César Hernández-Castro,

Arturo Ribagorda,

Pedro IsasiAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 1861 - 1862

https://doi.org/10.1145/1143997.1144300

Published: 08 July 2006 Publication History

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Abstract

The design of hash functions by means of evolutionary computation is a relatively new and unexplored problem. In this work, we use Genetic Programming (GP) to evolve robust and fast hash functions. We use a fitness function based on a non-linearity measure, producing evolved hashes with a good degree of Avalanche Effect. Efficiency is assured by using only very fast operators (both in hardware and software) and by limiting the number of nodes. Using this approach, we have created a new hash function, which we call gp-hash, that is able to outperform a set of five human-generated, widely-used hash functions.

References

[1]

Fowler / noll / vo (fnv) hash web page, http://www.isthe.com/chongo/tech/comp/fnv/.

Google Scholar

[2]

The lil-gp genetic programming system is available at http://garage.cps.msu.edu/software/lil-gp/lilgp-index.html.

Google Scholar

[3]

B. Jenkins. A hash function for hash table lookup. Dr.Dobbs Journal, September 1997.

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Cited By

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Verma RDhanda N(2023)Application of Supply Chain Management in Blockchain and IoT - A Generic Use Case2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence56041.2023.10048815(405-410)Online publication date: 19-Jan-2023
https://doi.org/10.1109/Confluence56041.2023.10048815
Grochol DSekanina L(2018)Fast Reconfigurable Hash Functions for Network Flow Hashing in FPGAs2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)10.1109/AHS.2018.8541401(257-263)Online publication date: Aug-2018
https://doi.org/10.1109/AHS.2018.8541401
Grochol DSekanina L(2018)Multi-objective Evolution of Ultra-Fast General-Purpose Hash FunctionsGenetic Programming10.1007/978-3-319-77553-1_12(187-202)Online publication date: 2-Mar-2018
https://doi.org/10.1007/978-3-319-77553-1_12
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Index Terms

Evolving hash functions by means of genetic programming
1. Information systems
  1. Information systems applications

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Abstract
Hash functions are a key component of many essential applications, ranging from compilers, databases or internet browsers to videogames or network devices. The same reduced set of functions are extensively used and have become “...
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Highlights
- We propose an automatic technique able to program tailor-made hash functions.
- ...

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Information & Contributors

Information

Published In

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2006

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Conference

GECCO06

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GECCO06: Genetic and Evolutionary Computation Conference

July 8 - 12, 2006

Washington, Seattle, USA

Acceptance Rates

GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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Verma RDhanda N(2023)Application of Supply Chain Management in Blockchain and IoT - A Generic Use Case2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence56041.2023.10048815(405-410)Online publication date: 19-Jan-2023
https://doi.org/10.1109/Confluence56041.2023.10048815
Grochol DSekanina L(2018)Fast Reconfigurable Hash Functions for Network Flow Hashing in FPGAs2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)10.1109/AHS.2018.8541401(257-263)Online publication date: Aug-2018
https://doi.org/10.1109/AHS.2018.8541401
Grochol DSekanina L(2018)Multi-objective Evolution of Ultra-Fast General-Purpose Hash FunctionsGenetic Programming10.1007/978-3-319-77553-1_12(187-202)Online publication date: 2-Mar-2018
https://doi.org/10.1007/978-3-319-77553-1_12
Grochol DSekanina L(2017)Multi-objective evolution of hash functions for high speed networks2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969485(1533-1540)Online publication date: 5-Jun-2017
https://dl.acm.org/doi/10.1109/CEC.2017.7969485
Ivanchykhin DIgnatchenko SLemire D(2017)Regular and almost universal hashingSoftware—Practice & Experience10.1002/spe.246147:10(1299-1323)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1002/spe.2461
Grochol DSekanina LNeumann FSutton A(2016)Evolutionary Design of Fast High-quality Hash Functions for Network ApplicationsProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908825(901-908)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908825
Langdon WHarman M(2015)Optimizing Existing Software With Genetic ProgrammingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2013.228154419:1(118-135)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1109/TEVC.2013.2281544
Lemire DKaser O(2015)Faster 64-bit universal hashing using carry-less multiplicationsJournal of Cryptographic Engineering10.1007/s13389-015-0110-56:3(171-185)Online publication date: 4-Sep-2015
https://doi.org/10.1007/s13389-015-0110-5
Estébanez CSaez YRecio GIsasi P(2014)AUTOMATIC DESIGN OF NONCRYPTOGRAPHIC HASH FUNCTIONS USING GENETIC PROGRAMMINGComputational Intelligence10.1111/coin.1203330:4(798-831)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1111/coin.12033
Karasek JBurget RDutta MSingh A(2014)Java evolutionary framework based on genetic programming2014 International Conference on Signal Processing and Integrated Networks (SPIN)10.1109/SPIN.2014.6777026(606-612)Online publication date: Feb-2014
https://doi.org/10.1109/SPIN.2014.6777026
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