ZHU Shaofeng, XI Wei, FAN Limin, et al., “Sequence-Oriented Stochastic Model of RO-TRNGs for Entropy Evaluation,” Chinese Journal of Electronics, vol. 29, no. 2, pp. 371-377, 2020, doi: 10.1049/cje.2019.12.010
Citation: ZHU Shaofeng, XI Wei, FAN Limin, et al., “Sequence-Oriented Stochastic Model of RO-TRNGs for Entropy Evaluation,” Chinese Journal of Electronics, vol. 29, no. 2, pp. 371-377, 2020, 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).
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  • 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.
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