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A parallelizable chaos-based true random number generator based on mobile device cameras for the Android platform


True random number generators are used in high security applications such as cryptography where non-determinism is required. However, they are slower than their pseudorandom counterparts because they need to extract entropy from physical phenomenon. To overcome this drawback, generators have been designed to extract unpredictability from devices such as computer processing units or microphones. This paper introduces a new generator for the Android mobile platform based on images captured by a built-in camera. Although similar generators exist, they suffer from poor performance and a lack of proper security evaluation. The proposed generator implements a chaos-based postprocessing algorithm that eliminates statistical defects and increases its throughput. These goals are achieved by using the inherent properties of a chaotic system to amplify entropy extracted from the captured images. The proposed generator is evaluated in two phases: first, statistical test suites are executed to identify statistical defects. Next, the generator’s forward and backward security is analysed. Results indicate that the proposed true random number generator is able to generate statistically secure true random number sequences faster than existing mobile-based generators. In addition, the generator is designed to support parallel processing, thus allowing its performance to scale according to the mobile device’s multicore architecture.

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  1. As of September, 2018.


  1. Addabbo T, Fort A, Rocchi S, Vignoli V (2009) Chaos based generation of true random bits. Springer, Berlin, pp 355–377

    MATH  Google Scholar 

  2. Android image format.

  3. Android distribution dashboard (2018),

  4. Aksoy S, Haralick RM (2001) Feature normalization and likelihood-based similarity measures for image retrieval. Pattern Recogn Lett 22(5):563–582. Image/Video Indexing and Retrieval

    Article  MATH  Google Scholar 

  5. Altaf M, Ahmad A, Khan FA, Uddin Z, Yang X (2018) Computationally efficient selective video encryption with chaos based block cipher. Multimedia Tools and Applications.

  6. Bassham LE, Rukhin AL, Soto J, Nechvatal JR, Smid ME, Leigh SD, Levenson M, Vangel M, Heckert NA, Banks DL (2010) A statistical test suite for random and pseudorandom number generators for cryptographic applications. Tech. rep., National Institute of Standards and Technology.

  7. Bouda J, Krhovjak J, Matyas V, Svenda P (2009) Towards true random number generation in mobile environments. In: Jøsang A, Maseng T, Knapskog SJ (eds) Identity and privacy in the internet age. Springer, Berlin, pp 179–189

  8. Brown RG (2018) dieharder.

  9. Carter J, Wegman MN (1979) Universal classes of hash functions. J Comput Syst Sci 18(2):143–154.

    MathSciNet  Article  MATH  Google Scholar 

  10. Cicek I, Pusane AE, Dundar G (2014) A novel design method for discrete time chaos based true random number generators. Integr VLSI J 47(1):38–47.

    Article  Google Scholar 

  11. Coron JS (1999) On the security of random sources. In: Public key cryptography. Springer, Berlin, pp 29–42

  12. Davis D, Ihaka R, Fenstermacher P (1994) Cryptographic randomness from air turbulence in disk drives. In: Desmedt YG (ed) Advances in cryptology — CRYPTO ’94. Springer, Berlin, pp 114–120

  13. Dodis Y, Pointcheval D, Ruhault S, Vergniaud D, Wichs D (2013) Security analysis of pseudo-random number generators with input: /dev/random is not robust. In: Proceedings of the 2013 ACM SIGSAC conference on computer & communications security, CCS ’13. ACM, New York, pp 647–658, DOI

  14. Gan Z, Chai X, Yuan K, Lu Y (2018) A novel image encryption algorithm based on lft based s-boxes and chaos. Multimed Tools Appl 77(7):8759–8783.

    Article  Google Scholar 

  15. Kanak A, Ergun S (2017) A practical biometric random number generator for mobile security applications. IEICE Trans Fund Electron Commun Comput Sci E100.A (1):158–166.

    Article  Google Scholar 

  16. Keuninckx L, Soriano MC, Fischer I, Mirasso CR, Nguimdo RM, der Sande GV (2017) Encryption key distribution via chaos synchronization. Sci Rep, 7(43428).

  17. Marsaglia G (1995) The marsaglia random number cdrom including the diehard battery of tests of randomness.

  18. Oteo JA, Ros J (2007) Double precision errors in the logistic map: statistical study and dynamical interpretation. Phys Rev E 76:036214.

    Article  Google Scholar 

  19. Sanguinetti B, Martin A, Zbinden H, Gisin N (2014) Quantum random number generation on a mobile phone. Phys Rev X 4:031056.

    Google Scholar 

  20. Schindler W, Killmann W (2003) Evaluation criteria for true (physical) random number generators used in cryptographic applications. In: Cryptographic hardware and embedded systems - CHES 2002, lecture notes in computer science, vol 2523. Springer, Berlin, pp 431–449.

  21. Suciu A, Lebu D, Marton K (2011) Unpredictable random number generator based on mobile sensors. In: 2011 IEEE 7th international conference on intelligent computer communication and processing, pp 445–448.

  22. Teh JS, Samsudin A, Akhavan A (2015) Parallel chaotic hash function based on the shuffle-exchange network. Nonlin Dyn 81(3):1067–1079.

    Article  Google Scholar 

  23. Teh JS, Samsudin A, Al-Mazrooie M, Akhavan A (2015) Gpus and chaos: a new true random number generator. Nonlin Dyn 82(4):1913–1922.

    MathSciNet  Article  Google Scholar 

  24. Walker J (2008) Pseudorandom number sequence test program.

  25. Wallace K, Moran K, Novak E, Zhou G, Sun K (2016) Toward sensor-based random number generation for mobile and iot devices. IEEE Internet Things J 3(6):1189–1201.

    Article  Google Scholar 

  26. Wei W, Guo H (2009) Bias-free true random-number generator. Opt Lett 34 (12):1876–1878.

    MathSciNet  Article  Google Scholar 

  27. Xingyuan W, Xue Q, Lin T (2012) A novel true random number generator based on mouse movement and a one-dimensional chaotic map. Mathematical Problems in Engineering

  28. Yoshizawa Y, Kimura H, Inoue H, Fujita K, Toyama M, Miyatake O (1999) Physical random numbers generated by radioactivity. J Japanese Soc Comput Statist, 2012.

  29. Zhang X, Qi L, Tang Z, Zhang Y (2014) Portable true random number generator for personal encryption application based on smartphone camera. Electron Lett 50(24):1841–1843.

    Article  Google Scholar 

  30. Zhao L, Liao X, Xiao D, Xiang T, Zhou Q, Duan S (2009) True random number generation from mobile telephone photo based on chaotic cryptography. Chaos, Solitons & Fractals 42(3):1692–1699.

    Article  Google Scholar 

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This work has been partially supported by Universiti Sains Malaysia under Grant No. 304/PKOMP/6315190 and the National Natural Science Foundation of China under Grant No. 61702212.

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Correspondence to Je Sen Teh.

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Yeoh, WZ., Teh, J.S. & Chern, H.R. A parallelizable chaos-based true random number generator based on mobile device cameras for the Android platform. Multimed Tools Appl 78, 15929–15949 (2019).

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  • True random number generator
  • Chaos theory
  • Android
  • Mobile device
  • Digital camera