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From Random Numbers to Random Objects.

Behrouz Zolfaghari1, Khodakhast Bibak2, Takeshi Koshiba3

  • 1Cyber Science Lab, School of Computer Science, University of Guelph, Guelph, ON N1G 2W1, Canada.

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Summary

This study introduces a unified method for generating random non-numerical objects, like passwords and CAPTCHAs. It connects random object generation to random number generation, offering solutions for diverse security applications.

Keywords:
Linear Feedback Shift Registers (LFSRs)S-restricted random number generatorinteger compositionsparallel LFSRsrandom number generationrandom object generation

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Area of Science:

  • Cryptography and Computer Science
  • Information Security and Randomness

Background:

  • Security applications require random generation of non-numerical entities like passwords, permutations, and CAPTCHAs.
  • Current methods address each entity type separately, lacking a unified approach.

Purpose of the Study:

  • To propose a unified framework for the random generation of diverse non-numerical entities.
  • To establish random object generation as a general problem solvable by linking it to random number generation.

Main Methods:

  • Formulating random object generation as a distinct problem.
  • Connecting the problem to the established field of random number generation.
  • Developing and analyzing methods to bridge these two areas.

Main Results:

  • A novel, unified approach to generating various random non-numerical entities.
  • Identification and resolution of challenges in linking random object and number generation.
  • Demonstration of the method's efficacy through a random Latin square generation case study.

Conclusions:

  • The proposed unified method offers a generalized solution for random non-numerical entity generation.
  • This approach simplifies and enhances the security of various applications reliant on random data.
  • Future work can extend this framework to other complex random generation tasks.