Random Walk in a N-Cube Without Hamiltonian Cycle to Chaotic Pseudorandom Number Generation: Theoretical and Practical Considerations

Designing a pseudorandom number generator (PRNG) is a difficult and complex task. Many recent works have considered chaotic functions as the basis of built PRNGs: the quality of the output would indeed be an obvious consequence of some chaos properties. However, there is no direct reasoning that goes from chaotic functions to uniform distribution of the output. Moreover, embedding such kind of functions into a PRNG does not necessarily allow to get a chaotic output, which could be required for simulating some chaotic behaviors. In a previous work, some of the authors have proposed the idea of walking into a N-cube where a balanced Hamiltonian cycle has been removed as the basis of a chaotic PRNG. In this article, all the difficult issues observed in the previous work have been tackled. The chaotic behavior of the whole PRNG is proven. The construction of the balanced Hamiltonian cycle is theoretically and practically solved. An upper bound of the expected length of the walk to obtain a uniform distributio...

[1]  Adrien Richard,et al.  On the Link between Strongly Connected Iteration Graphs and Chaotic Boolean Discrete-Time Dynamical Systems , 2011, FCT.

[2]  Pierre L'Ecuyer,et al.  TestU01: A C library for empirical testing of random number generators , 2006, TOMS.

[3]  R. Devaney An Introduction to Chaotic Dynamical Systems , 1990 .

[4]  Elaine B. Barker,et al.  Recommendation for the Transitioning of Cryptographic Algorithms and Key Sizes , 2010 .

[5]  I. S. Bykov,et al.  On locally balanced gray codes , 2016, Journal of Applied and Industrial Mathematics.

[6]  M. Mitzenmacher,et al.  Probability and Computing: Chernoff Bounds , 2005 .

[7]  Jacques M. Bahi,et al.  On the Design of a Family of Ci Pseudo-Random Number Generators , 2011, 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing.

[8]  Carla D. Savage,et al.  Balanced Gray Codes , 1996, Electron. J. Comb..

[9]  L. Kocarev,et al.  Chaos-based random number generators-part I: analysis [cryptography] , 2001 .

[10]  Elizabeth L. Wilmer,et al.  Markov Chains and Mixing Times , 2008 .

[11]  John P. Robinson,et al.  Counting sequences , 1981, IEEE Transactions on Computers.

[12]  Jacques M. Bahi,et al.  Pseudorandom number generators with balanced Gray codes , 2014, 2014 11th International Conference on Security and Cryptography (SECRYPT).

[13]  L. Kocarev,et al.  Chaos-based random number generators. Part II: practical realization , 2001 .

[14]  AJ van Zanten,et al.  Totally balanced and exponentially balanced Gray codes , 2004 .

[15]  Jacques M. Bahi,et al.  Improving random number generators by chaotic iterations application in data hiding , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[16]  J. Banks,et al.  On Devaney's definition of chaos , 1992 .

[17]  Lequan Min,et al.  A Chaos-Based Pseudorandom Number Generator and Performance Analysis , 2009, 2009 International Conference on Computational Intelligence and Security.

[18]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.