A Secure Server-Based Pseudorandom Number Generator Protocol for Mobile Devices

  • Hooman Alavizadeh
  • Hootan Alavizadeh
  • Kudakwashe Dube
  • Dong Seong Kim
  • Julian Jang-Jaccard
  • Hans W. Guesgen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10701)


Mobile devices play an essential role in telecommunication era. The need for securing this type of communications is inevitable. The majority of security and cryptographic protocols require unpredictable random numbers. However, mobile computing devices have difficulty in generating random numbers due to constraints in terms of power and computing resources. We propose a novel pseudorandom number generator protocol to enable secure communication between mobile devices and a trusted centralized server. The trusted centralized server generates qualified random numbers based on the location of mobile device specified by geographical latitude and longitude. We evaluate the quality of generated random bit sequences through the National Institute of Standards and Technology (NIST) tests, and compare them with other methods in regard to security and quality of generated random numbers. The quality of the randomness of generated numbers is comparable to that from the existing methods and more superior than them found in use in mobile devices today.


Geographical latitude and longitude Key management Mobile security Pseudorandom number generator 


  1. 1.
    Agarwal, R., Agarwal, G.: An efficient method of generating random numbers from congruence equations for cryptographic applications. Int. J. Sci. Eng. Comput. Technol. 6(7), 290 (2016)Google Scholar
  2. 2.
    Bazai, S.U., Jang-Jaccard, J., Zhang, X.: A privacy preserving platform for MapReduce. In: Batten, L., Kim, D.S., Zhang, X., Li, G. (eds.) ATIS 2017. CCIS, vol. 719, pp. 88–99. Springer, Singapore (2017). CrossRefGoogle Scholar
  3. 3.
    Bhaskar, P., Gawande, P.: A survey on implementation of random number generator in FPGA. Int. J. Sci. Res. (IJSR) 1590–1592 (2013)Google Scholar
  4. 4.
    Blum, M., Micali, S.: How to generate cryptographically strong sequences of pseudorandom bits. SIAM J. Comput. 13(4), 850–864 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Callegari, S., Rovatti, R., Setti, G.: Embeddable ADC-based true random number generator for cryptographic applications exploiting nonlinear signal processing and chaos. IEEE Trans. Signal Process. 53(2), 793–805 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Chefranov, A., Abhari, S.M.A., Alavizadeh, H., Zanjani, M.F.: Secure true random number generator in WLAN/LAN. In: Proceedings of the 6th International Conference on Security of Information and Networks, pp. 331–335. ACM (2013)Google Scholar
  7. 7.
    Francillon, A., Castelluccia, C.: Tinyrng: A cryptographic random number generator for wireless sensors network nodes. In: 2007 5th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks and Workshops, WiOpt 2007, pp. 1–7. IEEE (2007)Google Scholar
  8. 8.
    Kozierski, P., Lis, M., Królikowski, A.: Parallel uniform random number generator in FPGA. Comput. Appl. Electr. Eng. 12 (2014)Google Scholar
  9. 9.
    LEcuyer, P., Munger, D., Oreshkin, B., Simard, R.: Random numbers for parallel computers: requirements and methods, with emphasis on GPUs. Math. Comput. Simul. 135, 3–17 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Lee, J.-H., Jeon, M.-J., Kim, S.C.: Uniform random number generator using leap-ahead LFSR architecture. In: Kim, T., Ramos, C., Kim, H., Kiumi, A., Mohammed, S., Ślęzak, D. (eds.) ASEA 2012. CCIS, vol. 340, pp. 264–271. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  11. 11.
    Lo Re, G., Milazzo, F., Ortolani, M.: Secure random number generation in wireless sensor networks. Concurrency Comput. Pract. Experience 27(15), 3842–3862 (2015)CrossRefGoogle Scholar
  12. 12.
    Noll, L.C., Mende, R.G., Sisodiya, S.: Method for seeding a pseudo-random number generator with a cryptographic hash of a digitization of a chaotic system. US Patent 5,732,138, 24 March 1998Google Scholar
  13. 13.
    Pareschi, F., Setti, G., Rovatti, R.: Implementation and testing of high-speed cmos true random number generators based on chaotic systems. IEEE Trans. Circuits Syst. I Regul. Pap. 57(12), 3124–3137 (2010)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Rukhin, A., Soto, J., Nechvatal, J., Barker, E., Leigh, S., Levenson, M., Banks, D., Heckert, A., Dray, J., Vo, S., et al.: Statistical test suite for random and pseudorandom number generators for cryptographic applications, NIST Special Publication (2010)Google Scholar
  15. 15.
    Sathyamorthy, D., Shafii, S., Amin, Z.F.M., Jusoh, A., Ali, S.Z.: Evaluation of the trade-off between global positioning system (GPS) accuracy and power saving from reduction of number of GPS receiver channels. Appl. Geomatics 8(2), 67–75 (2016)CrossRefGoogle Scholar
  16. 16.
    Shujun, L., Xuanqin, M., Yuanlong, C.: Pseudo-random bit generator based on couple chaotic systems and its applications in stream-cipher cryptography. In: Rangan, C.P., Ding, C. (eds.) INDOCRYPT 2001. LNCS, vol. 2247, pp. 316–329. Springer, Heidelberg (2001). CrossRefGoogle Scholar
  17. 17.
    Stefanov, A., Gisin, N., Guinnard, O., Guinnard, L., Zbinden, H.: Optical quantum random number generator. J. Mod. Opt. 47(4), 595–598 (2000)Google Scholar
  18. 18.
    Suo, H., Wan, J., Zou, C., Liu, J.: Security in the internet of things: a review. In: 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE), vol. 3, pp. 648–651. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Natural and Mathematical SciencesMassey UniversityAucklandNew Zealand
  2. 2.Computer EngineeringImamreza UniversityMashhadIran
  3. 3.School of Engineering and Advanced TechnologyMassey UniversityPalmerston NorthNew Zealand
  4. 4.Computer Science and Software EngineeringUniversity of CanterburyChristchurchNew Zealand

Personalised recommendations