Sequence‐Oriented Stochastic Model of RO‐TRNGs for Entropy Evaluation. Issue 2 (1st March 2020)

• Record Type: Journal Article
• Authors: Zhu, Shaofeng; Xi, Wei; Fan, Limin; Chen, Hua; Chen, Meihui; Feng, Dengguo
• Is Part Of: Chinese journal of electronics. Volume 29:Issue 2(2020)
• Page Start: 371
• Page End: 377
• Publication Date: 2020-03-01
• Subjects: True random number generator (TRNG) -- Ring oscillator -- Entropy -- Stochastic model -- Randomness; entropy -- logic circuits -- Markov processes -- oscillators -- random number generation -- stochastic processes; cryptographic systems -- elementary‐RO‐TRNG -- multiple‐RO‐TRNG -- ring oscillator -- random number generators -- unpredictable random numbers -- first‐order stationary Markov source model -- entropy evaluation -- sequence‐oriented stochastic model -- phase‐oriented ones -- time‐oriented model; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2019.12.010
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.