ZHU Shaofeng, XI Wei, FAN Limin, CHEN Hua, CHEN Meihui, FENG Dengguo. Sequence-Oriented Stochastic Model of RO-TRNGs for Entropy Evaluation[J]. Chinese Journal of Electronics, 2020, 29(2): 371-377. doi: 10.1049/cje.2019.12.010
Citation: ZHU Shaofeng, XI Wei, FAN Limin, CHEN Hua, CHEN Meihui, FENG Dengguo. Sequence-Oriented Stochastic Model of RO-TRNGs for Entropy Evaluation[J]. Chinese Journal of Electronics, 2020, 29(2): 371-377. doi: 10.1049/cje.2019.12.010

Sequence-Oriented Stochastic Model of RO-TRNGs for Entropy Evaluation

doi: 10.1049/cje.2019.12.010
Funds:  This work is supported by the the Nation Key Research and Development Program of China (No.2018YFB0904900, No.2018YFB0904901).
More Information
  • Corresponding author: FAN Limin (corresponding author) received the Ph.D. degree from Chinese Academy of Sciences in 2010. She is a masters supervisor of Institute of Software, Chinese Academy of Sciences. Her research interests include cryptology evaluation theory and technology. (Email:fanlimin@tca.iscas.ac.cn)
  • Received Date: 2018-12-30
  • Rev Recd Date: 2019-07-10
  • Publish Date: 2020-03-10
  • Ring oscillator-based true random number generators (RO-TRNGs) are widely used to generate unpredictable random numbers for cryptographic systems. Entropy is usually adopted to quantitatively measure the unpredictability of a TRNG. There have been several stochastic models such as the time-oriented and phaseoriented ones built to evaluate the entropy of ElementaryRO-TRNGs with single oscillator. However, these models are not suitable for the TRNGs composed of multiple oscillators (Multiple-RO-TRNGs), which can obtain more randomness and higher throughput. Considering this, we propose a sequence-oriented stochastic model for the entropy evaluation of RO-TRNGs, named the first-order stationary Markov source model. This model is extensible for the Multiple-RO-TRNGs. Based on that, we present a detailed method to determine the entropy of Multiple-ROTRNGs. Our proposed model is verified by experiments. Besides, our method can also be a guide to design ROTRNGs with both high entropy and high throughput.
  • loading
  • P. Wang, Z. Li, G. Li, X. Cheng, et al., “Design of true random number generator based on VCO”, Acta Electronica Sinica, Vol.47, No.2, pp.417-421, 2019. (in Chinese)
    B. Li, C. Zhou, S. Chen, et al., “FPGA Implementation of Pseudo-Random Number Generator for SRAM PUFs”, Acta Electronica Sinica, Vol.45, No.9, pp.2106-2112, 2017. (in Chinese)
    B. Valtchanov, A. Aubert, F. Bernard, et al., “Modeling and observing the jitter in ring oscillators implemented in FPGAs”, Proc. of the 11th IEEE Workshop on Design and Diagnostics of Electronic Circuits and Systems, Bratislava, Slovakia, pp.158-163, 2008.
    B. Sunar, W.J. Martin and D.R. Stinson, “A provably secure true random number generator with built-in tolerance to active attacks”, IEEE Trans. Computers, Vol.56, No.1, pp.109-119, 2007.
    K. Wold and C.H. Tan, “Analysis and enhancement of random number generator in FPGA based on oscillator rings”, International Conference on Reconfigurable Computing and FPGAs, Washington, DC, USA pp.385-390, 2008.
    W. Killmann and W. Schindler, “A Design for a physical RNG with robust entropy estimators”, Cryptographic Hardware and Embedded Systems, Washington, DC, USA, pp.146-163, 2008.
    Y. Ma, J. Lin, T. Chen, et al., “Entropy evaluation for oscillator-based true random number generators”, Cryptographic Hardware and Embedded Systems, Busan, South Korea, pp.544-561, 2014.
    M. Baudet, D. Lubicz, J. Micolod, et al., “On the security of oscillator-based random number generators”, Journal of Cryptology, Vol.24, No.2, pp.398-425, 2011.
    T. Amaki, M. Hashimoto, Y. Mitsuyama, et al., “A design procedure for oscillator-based hardware random number generator with stochastic behavior modeling”, Proc. of the 11th International Conference on Information Security Applications, Jeju Island, Korea, pp.107-121, 2010.
    Y. Ma, J. Lin and J. Jing, “On the entropy of oscillator-based true random number generators”, Topics in Cryptology-CTRSA 2017, San Francisco, CA, USA, pp.165-180, 2017.
    T.W. Aderson and L.A. Goodman, “Statistical inference about Markov chains”, Annals of Mathematical Statistics, Vol.28, No.1, pp.89-110, 1957.
    B. Tan and K. Yilmaz, “Markov chain test for time dependence and homogeneity: An analytical and empirical evaluation”, European Journal of Operational Research, Vol.137, No.3, pp.524-543, 2002.
    S.K. Yoo, D. Karakoyunlu, B. Birand, et al., “Improving the Robustness of Ring Oscillator TRNGs”, ACM Transactions on Reconfigurable Technology and Systems, Vol.3, No.2, pp.9:1-9:30, 2010.
    P. Haddad, Y. Teglia, F. Bernard, et al., “On the assumption of mutual independence of jitter realizations in P-TRNG stochastic models”, Design, Automation and Test in Europe Conference and Exhibition, Dresden, Germany, pp.1-6, 2014.
    W. Killmann and W. Schindler, “AIS 31: A proposal for: Functionality classes for random number generators Version 2.0”, available at https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/Zertifizierung/Interpretationen/AIS_31_Functionality_classes_for_random_number_generators_e.html, 2011-9-21.
    A. Rukhin, J. Soto, J. Nechvatal, et al., “ A statistical test suite for random and pseudorandom number generators for cryptographic applications”, available at http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-22r1a.pdf, 2010-4-27.
    M.S. Turan, E. Barker, J. Kelsey, et al., “Recommendation for the entropy sources used for random bit generation”, available at https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-90B.pdf, 2018-1.
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (58) PDF downloads(92) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint