Please wait a minute...
Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2020, Vol. 14 Issue (6) : 146405    https://doi.org/10.1007/s11704-019-9120-2
RESEARCH ARTICLE
A post-processing method for true random number generators based on hyperchaos with applications in audio-based generators
Je Sen TEH1(), Weijian TENG2, Azman SAMSUDIN1, Jiageng CHEN3
1. School of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
2. School of Engineering and Technology, INTI International College Penang, Penang 11900, Malaysia
3. School of Computer, Central China Normal University,Wuhan 430079, China
 Download: PDF(906 KB)  
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

True random number generators (TRNG) are important counterparts to pseudorandom number generators (PRNG), especially for high security applications such as cryptography. They produce unpredictable, non-repeatable random sequences. However, most TRNGs require specialized hardware to extract entropy from physical phenomena and tend to be slower than PRNGs. These generators usually require post-processing algorithms to eliminate biases but in turn, reduces performance. In this paper, a new post-processing method based on hyperchaos is proposed for software-based TRNGs which not only eliminates statistical biases but also provides amplification in order to improve the performance of TRNGs. The proposed method utilizes the inherent characteristics of chaos such as hypersensitivity to input changes, diffusion, and confusion capabilities to achieve these goals. Quantized bits of a physical entropy source are used to perturb the parameters of a hyperchaotic map, which is then iterated to produce a set of random output bits. To depict the feasibility of the proposed post-processing algorithm, it is applied in designing TRNGs based on digital audio. The generators are analyzed to identify statistical defects in addition to forward and backward security. Results indicate that the proposed generators are able to produce secure true random sequences at a high throughput,which in turn reflects on the effectiveness of the proposed post-processing method.

Keywords audio      chaos theory      chaotic map      entropy      hy-perchaos      post-processing      random number generator      security     
Corresponding Author(s): Je Sen TEH   
Just Accepted Date: 11 September 2019   Issue Date: 20 July 2020
 Cite this article:   
Je Sen TEH,Weijian TENG,Azman SAMSUDIN, et al. A post-processing method for true random number generators based on hyperchaos with applications in audio-based generators[J]. Front. Comput. Sci., 2020, 14(6): 146405.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-019-9120-2
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I6/146405
1 O Cret, T Gyorfi, A Suciu. Implementing true random number generators based on high fanout nets. Romanian Journal of Information Science and Technology, 2012, 15(3): 277–298
2 B Jun, P Kocher. The intel random number generator. Cryptography Research Inc. White Paper, 1999, 27: 1–8
3 I Cicek, A E Pusane, G Dundar. An integrated dual entropy core true random number generator. IEEE Transactions on Circuits and Systems II: Express Briefs, 2017, 64(3): 329–333
https://doi.org/10.1109/TCSII.2016.2568181
4 B Karakaya, V Çelik, A Gulten. Chaotic cellular neural network-based true random number generator. International Journal of Circuit Theory and Applications, 2017, 45(11): 1885–1897
https://doi.org/10.1002/cta.2374
5 T Bonny, R A Debsi, S Majzoub, A S Elwakil. Hardware optimized FPGA implementations of high-speed true random bit generators basedon switching-type chaotic oscillators. Circuits, Systems, and Signal Processing, 2018, 38(3): 1342–1359
https://doi.org/10.1007/s00034-018-0905-6
6 F Mei, L Zhang, C Gu, Y Cao, C Wang, W Liu. A highly flexible lightweight and high speed true random number generator on FPGA. In: Proceedings of IEEE Computer Society Annual Symposium on VLSI (ISVLSI). 2018
https://doi.org/10.1109/ISVLSI.2018.00079
7 T T N Nguyen, G Kaddoum, F Gagnon. Implementation of a chaotic true random number generator based on fuzzy modeling. In: Proceedings of the 16th IEEE International New Circuits and Systems Conference. 2018
8 D Kumar, K Nabi, P K Misra, M Goswami. Modified tent map based design for true random number generator. In: Proceedings of IEEE International Symposium on Smart Electronic Systems. 2018
https://doi.org/10.1109/iSES.2018.00016
9 M Alcin, I Koyuncu, M Tuna, M Varan, I Pehlivan. A novel high speed artificial neural network-based chaotic true random number generator on field programmable gate array. International Journal of Circuit Theory and Applications, 2018, 47(3): 365–378
https://doi.org/10.1002/cta.2581
10 J C Hsueh, V H C Chen. An ultra-low voltage chaos-based true random number generator for IoT applications. Microelectronics Journal, 2019, 87: 55–64
https://doi.org/10.1016/j.mejo.2019.03.013
11 R Gupta, A Pandey, R K Baghel. FPGA implementation of chaosbased high-speed true random number generator. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2019, 32(5): e2604
https://doi.org/10.1002/jnm.2604
12 B Karakaya, A Gulten, M Frasca. A true random bit generator based on a memristive chaotic circuit: analysis, design and FPGA implementation. Chaos, Solitons & Fractals, 2019, 119: 143–149
https://doi.org/10.1016/j.chaos.2018.12.021
13 J S Teh, A Samsudin, M Al-Mazrooie, A Akhavan. GPUs and chaos: a new true random number generator. Nonlinear Dynamics, 2015, 82(4): 1913–1922
https://doi.org/10.1007/s11071-015-2287-7
14 D Davis, R Ihaka, P Fenstermacher. Cryptographic randomness from air turbulence in disk drives. In: Proceedings of Annual International Cryptology Conference. 1994, 114–120
https://doi.org/10.1007/3-540-48658-5_13
15 Y Hu, X Liao, K wo Wong, Q Zhou. A true random number generator based on mouse movement and chaotic cryptography. Chaos, Solitons & Fractals, 2009, 40(5): 2286–2293
https://doi.org/10.1016/j.chaos.2007.10.022
16 W Xingyuan, Q Xue, T Lin. A novel true random number generator based on mouse movement and a one-dimensional chaotic map. Mathematical Problems in Engineering, 2012
https://doi.org/10.1155/2012/931802
17 W Z Yeoh, J S Teh, H R Chern. A parallelizable chaos-based true random number generator based on mobile device cameras for the android platform. Multimedia Tools and Applications, 2019, 78(12): 15929–15949
https://doi.org/10.1007/s11042-018-7015-0
18 S Nikolic, M Veinovic. Advancement of true random number generators based on sound cards through utilization of a new post-processing method. Wireless Personal Communications, 2016, 91(2): 603–622
https://doi.org/10.1007/s11277-016-3480-9
19 R B Davies. Exclusive OR (XOR) and hardware random number generators. see Wikipedia, 2002
20 J Von Neumann. Various techniques used in connection with random digits. National Bureau of Standards Applied Mathematical Series, 1951, 12(36–38): 5
21 P Lacharme. Post-processing functions for a biased physical random number generator. In: Proceedings of International Workshop on Fast Software Encryption. 2008, 334–342
https://doi.org/10.1007/978-3-540-71039-4_21
22 E Avaroğlu, T Tuncer, A Őzer, B Ergen, M Tűrk. A novel chaos-based post-processing for TRNG. Nonlinear Dynamics, 2015, 81(1–2): 189–199
https://doi.org/10.1007/s11071-015-1981-9
23 W Schindler, W Killmann. Evaluation criteria for true (physical) random number generators used in cryptographic applications. In: Proceedings of International Workshop on Cryptographic Hardware and Embedded Systems. 2002, 431–449
https://doi.org/10.1007/3-540-36400-5_31
24 B Sunar, W J Martin, D R Stinson. A provably secure true random number generator with built-in tolerance to active attacks. IEEE Transactions on Computers, 2007, 56(1): 109–119
https://doi.org/10.1109/TC.2007.250627
25 S H Kwok, Y L Ee, G Chew, K Zheng, K Khoo, C H Tan. A comparison of post-processing techniques for biased random number generators. In: Proceedings of IFIP International Workshop on Information Security Theory and Practices. 2011, 175–190
https://doi.org/10.1007/978-3-642-21040-2_12
26 M Ahmad, S Khurana, S Singh, H D AlSharari. A simple secure hash function scheme usingmultiple chaotic maps. 3D Research, 2017, 8(2): 13
https://doi.org/10.1007/s13319-017-0123-1
27 Y Li, G Ge. Cryptographic and parallel hash function based on cross coupled map lattices suitable for multimedia communication security. Multimedia Tools and Applications, 2019, 78(13): 17973–17994
https://doi.org/10.1007/s11042-018-7122-y
28 A ur Rehman, X Liao. A novel robust dual diffusion/confusion encryption technique for color image based on chaos, DNA and SHA-2. Multimedia Tools and Applications, 2018, 78(2): 2105–2133
https://doi.org/10.1007/s11042-018-6346-1
29 Z Xiong, Y Wu, C Ye, X Zhang, F Xu. Color image chaos encryption algorithm combining CRC and nine palace map. Multimedia Tools and Applications, 2019, 78(22): 31035–31055
https://doi.org/10.1007/s11042-018-7081-3
30 M Garcia-Bosque, A Perez-Resa, C Sanchez-Azqueta, C Aldea, S Celma. Chaos-based bitwise dynamical pseudorandom number generator on FPGA. IEEE Transactions on Instrumentation and Measurement, 2019, 68(1): 291–293
https://doi.org/10.1109/TIM.2018.2877859
31 A Rukhin, J Soto, J Nechvatal. A statistical test suite for random and pseudorandom number generators for cryptographic applications. National Institute of Standards, NIST Special Publication 800-22, 2010
32 G Marsaglia. DIEHARD: a battery of tests of Randomness. 1996
33 J Walker. ENT Program. 2008
34 J S Teh, W Teng, A Samsudin. A true random number generator based on hyperchaos and digital sound. In: Proceedings of the 3rd International Conference on Computer and Information Sciences. 2016, 264–269
https://doi.org/10.1109/ICCOINS.2016.7783225
35 Y Dodis, D Pointcheval, S Ruhault, D Vergniaud, D Wichs. 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. 2013, 647–658
https://doi.org/10.1145/2508859.2516653
36 J S Coron. On the security of random sources. In: Proceedings of International Workshop on Public Key Cryptography. 1999, 29–42
https://doi.org/10.1007/3-540-49162-7_3
37 R Benítez, V Bolós, M Ramírez. A wavelet-based tool for studying non-periodicity. Computers & Mathematics with Applications, 2010, 60(3): 634–641
https://doi.org/10.1016/j.camwa.2010.05.010
38 T Ritter. The efficient generation of cryptographic confusion sequences. Cryptologia, 1991, 15(2): 81–139
https://doi.org/10.1080/0161-119191865812
39 S W Golomb. Shift register sequences. World Scientific. 2014
https://doi.org/10.1142/9361
40 J Massey. Shift-register synthesis and BCH decoding. IEEE Transactions on Information Theory, 1969, 15(1): 122–127
https://doi.org/10.1109/TIT.1969.1054260
41 A J Menezes, P C van Oorschot, S A Vanstone. Handbook of Applied Cryptography. CRC Press, 2018
https://doi.org/10.1201/9780429466335
42 N G Bardis, A P Markovskyi, N Doukas, N V Karadimas. True random number generation based on environmental noise measurements for military applications. In: Proceedings of the 8th WSEAS International Conference on Signal Processing, Robotics and Automation. 2009, 68–73
[1] Bin GUO, Yasan DING, Yueheng SUN, Shuai MA, Ke LI, Zhiwen YU. The mass, fake news, and cognition security[J]. Front. Comput. Sci., 2021, 15(3): 153806-.
[2] Abhishek MAJUMDAR, Arpita BISWAS, Atanu MAJUMDER, Sandeep Kumar SOOD, Krishna Lal BAISHNAB. A novel DNA-inspired encryption strategy for concealing cloud storage[J]. Front. Comput. Sci., 2021, 15(3): 153807-.
[3] Zeli WANG, Hai JIN, Weiqi DAI, Kim-Kwang Raymond CHOO, Deqing ZOU. Ethereum smart contract security research: survey and future research opportunities[J]. Front. Comput. Sci., 2021, 15(2): 152802-.
[4] Xiaochen LIU, Chunhe XIA, Tianbo WANG, Li ZHONG, Xiaojian LI. A behavior-aware SLA-based framework for guaranteeing the security conformance of cloud service[J]. Front. Comput. Sci., 2020, 14(6): 146808-.
[5] Yanwei ZHOU, Bo YANG. Practical continuous leakage-resilient CCA secure identity-based encryption[J]. Front. Comput. Sci., 2020, 14(4): 144804-.
[6] Yudi ZHANG, Debiao HE, Mingwu ZHANG, Kim-Kwang Raymond CHOO. A provable-secure and practical two-party distributed signing protocol for SM2 signature algorithm[J]. Front. Comput. Sci., 2020, 14(3): 143803-.
[7] Xingyue CHEN, Tao SHANG, Feng ZHANG, Jianwei LIU, Zhenyu GUAN. Dynamic data auditing scheme for big data storage[J]. Front. Comput. Sci., 2020, 14(1): 219-229.
[8] Tianyong WU, Xi DENG, Jun YAN, Jian ZHANG. Analyses for specific defects in Android applications: a survey[J]. Front. Comput. Sci., 2019, 13(6): 1210-1227.
[9] Yan ZHU, Khaled RIAD, Ruiqi GUO, Guohua GAN, Rongquan FENG. New instant confirmation mechanism based on interactive incontestable signature in consortium blockchain[J]. Front. Comput. Sci., 2019, 13(6): 1182-1197.
[10] Sa WANG, Yiwen SHAO, Yungang BAO. Practices of backuping homomorphically encrypted databases[J]. Front. Comput. Sci., 2019, 13(2): 220-230.
[11] Rizwan Ahmed KHAN, Alexandre MEYER, Hubert KONIK, Saida BOUAKAZ. Saliency-based framework for facial expression recognition[J]. Front. Comput. Sci., 2019, 13(1): 183-198.
[12] Wei GAO, Guilin WANG, Kefei CHEN, Xueli WANG. Efficient identity-based threshold decryption scheme from bilinear pairings[J]. Front. Comput. Sci., 2018, 12(1): 177-189.
[13] Lip Yee POR, Chin Soon KU, Amanul ISLAM, Tan Fong ANG. Graphical password: prevent shoulder-surfing attack using digraph substitution rules[J]. Front. Comput. Sci., 2017, 11(6): 1098-1108.
[14] Sudipta ROY, Debnath BHATTACHARYYA, Samir Kumar BANDYOPADHYAY, Tai-Hoon KIM. An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction[J]. Front. Comput. Sci., 2017, 11(4): 717-727.
[15] Yougen YUAN, Lei XIE, Zhong-Hua FU, Ming XU, Qi CONG. Sound image externalization for headphone based real-time 3D audio[J]. Front. Comput. Sci., 2017, 11(3): 419-428.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed