Manuscript Number : CSEIT172527
Performance Enhancement of Cryptographic Algorithms by Increasing Randomness through Nesting In Random Number Generators
Authors(2) :-Antu Annam Thomas, Varghese Paul
Randomness is a term that is commonly used in applications like cryptography. In cryptography randomness finds its role in seed value generation. There are various traditional random number generators. Linear Congruential Generator (LCG) is one among them. In order to improve the efficiency of the generated series concept of nesting is introduced into LCG to form Nested Linear Congruential Generator (NLCG). In this paper LCG is compared with NLCG. In the case of LCG the period of the generated sequence will be depending upon the value of increment and multiplicand chosen. But in the case of NLCG the period is always infinity. This is the greatest advantage of NLCG when compared to LCG. Both statistical and graphical analysis is done. The discussion proves that efficiency of the generated series has been improved when nesting is introduced, thus in turn improving the efficiency of the cryptographic algorithms.
Antu Annam Thomas
Department of Computer Application, Mar Thoma College, Thiruvalla, Kerala, India
Department of Information Technology, Rajagiri School of Engineering and Technology, Rajagiri Valley, Kakkanad, Kerala, India
Cryptography; Data Security; True Random Number Generator; Pseudo random number generator; Linear Congruential Generator; Prime number; Kolmogorov Smirnov Test; Runs Test
- B. Schneier, “Applied cryptography: protocols, algorithms, and source code in C,” Second Edition, John Wiley & Sons, 1996.
- D. Dilli, Madhu S., “Design of a New Cryptography Algorithm using Reseeding -Mixing Pseudo Random Number Generator,” IJITEE, vol.52, No. 5, 2013
- K. Marton, A. Suciu, C. Sacarea, and Octavian Cret, “Generation and Testing of Random Numbers for Cryptographic Applications,” Proceedings of the Ramanian Academy, Series A, Vol. 13, No. 4, 2012, PP 368–377.
- Wikipedia, “Pseudorandom number generator”, Last visited December 2014.
- D. Dilli, and S. Madhu, “Design of a New Cryptography Algorithm using Reseeding -Mixing Pseudo Random Number Generator,” IJITEE, vol. 52, no. 5, 2013.
- “True Random Number Generators Secure in a Changing Environment”, Boaz Barak, Ronen Shaltiel, and Eran Tromer, Department of Computer Science and Applied Mathematics, Weizmann Institute of Science , Rehovot, ISRAEL
- David DiCarlo, “Random Number Generation: Types and Techniques,” A Senior Thesis submitted in partial fulfillment of the requirements for graduation in the Honors Program Liberty University Spring 2012.
- McNichol, Tom (2003-08-11). "Totally Random". Conde Nast Publications. p. 2. Retrieved 2009-10-23. Mads Haahr, a lecturer in computer science at Trinity College in Dublin, designed the system
- T. Simul, S.M. Assad, P.K. Lam “Real time demonstration of high bitrate quantum random number generation with coherent laser light”, Appl Phys Lett 98:231103-1-3
- Atsushi Uchida, Kazuya Amano, Masaki Inoue, Kunihito Hirano, Sunao Naito, Hiroyuki Someya, Isao Oowada, Takayuki Kurashige, Masaru Shiki, Shigeru Yoshimori, Kazuyuki Yoshimura & Peter Davis, “Fast physical random bit generation with chaotic semiconductor lasers”, Nature Photonics 2, 728 - 732 (2008)
- Sunar, B., Martin, W.J., Stinson, D.R. “A Provably Secure True Random Number Generator with Built-In Tolerance to Active Attacks”, Computers, IEEE Transactions on (Volume:56 , Issue: 1 ), Jan. 2007, pp. 109 – 119
- Hamed Rahimov, Majid Babaie, Hassan Hassanabadi, “Improving Middle Square Method RNG Using Chaotic Map”, Applied Mathematics, 2011, 2, 482-486
- Chan, H. “Random number generation”. Retrieved 10/16/2011fromhttp://fuchun00.dyndns.org/~mcmintro/random.pdf, 2009.
- Nishimura, T, “Tables of 64-bit mersenne twisters” , ACM Transactions on Modeling and Computer Simulation, 10(4), 348-357, 2000.
- Adi A. Maaita, Hamza A. A. Al_Sewadi, “Deterministic Random Number Generator Algorithm for Cryptosystem Keys”, International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:9, No:4, pp 972-977, 2015
- "Linear Congruential Generators" by Joe Bolte, Wolfram Demonstrations Project.
- Donald E. Knuth (6 May 2014). Art of Computer Programming, Volume 2: Seminumerical Algorithms. Addison-Wesley Professional. pp. 4. ISBN 978-0-321-63576-1.
- “Testing Random Number Generators”, Dan Biebighauser University of Minnesota - Twin Cities REU Summer 2000
- Antu Annam Thomas and Varghese Paul, “Random Number Geneeration Methods a Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 1, January 2016, pp.556-559
- Antu Annam Thomas and Varghese Paul, “Nested Random Number Generator”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 7, Issue 5, May 2017, pp.767-773
Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 203-209
Manuscript Number : CSEIT172527
Publisher : Technoscience Academy
URL : http://ijsrcseit.com/CSEIT172527