An ultra-low voltage chaos-based true random number generator for IoT applications

Hsueh, Jen-Chieh; Chen, Vanessa H.-C.

doi:10.1016/j.mejo.2019.03.013

Cited by 30 publications

(11 citation statements)

References 21 publications

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“…This TRNG chip can be mounted on the security module of small devices like the drones or IoT devices, and can be carried by individuals in the form of USB for banking or authentication. Looking at recent papers on TRNG for IoT applications [22,23], it emphasizes that it must be low power in hardware and pass statistical quality tests in accordance with NIST standards. Recently, a physically unclonable function (PUF), a kind of hardware-based RNG, has been widely used to secure IoT devices.…”

Section: Discussionmentioning

confidence: 99%

A lightweight true random number generator using beta radiation for IoT applications

Park

Choi

et al. 2020

ETRI Journal

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This paper presents a lightweight true random number generator (TRNG) using beta radiation that is useful for Internet of Things (IoT) security. In general, a random number generator (RNG) is required for all secure communication devices because random numbers are needed to generate encryption keys. Most RNGs are computer algorithms and use physical noise as their seed. However, it is difficult to obtain physical noise in small IoT devices. Since IoT security functions are required in almost all countries, IoT devices must be equipped with security algorithms that can pass the cryptographic module validation programs of each country. In this regard, it is very cumbersome to embed security algorithms, random number generation algorithms, and even physical noise sources in small IoT devices. Therefore, this paper introduces a lightweight TRNG comprising a thin-film beta-radiation source and integrated circuits (ICs). Although the ICs are currently being designed, the IC design was functionally verified at the board level. Our random numbers are output from a verification board and tested according to National Institute of Standards and Technology standards.

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Section: Discussionmentioning

confidence: 99%

A lightweight true random number generator using beta radiation for IoT applications

Park

Choi

et al. 2020

ETRI Journal

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“…Some of the recent developments in field of random and pseudo-random number generators have been reviewed. For example, the random number generation in [26,27] show the applications in BIST and IoT Applications. The later one is based on chaos and is validated by statistical tests.…”

Section: Related Workmentioning

confidence: 99%

“…We chose some recent works from the literature for comparing the results and to validate the randomness efficiency of SRFG. The algorithms described in [26,27,29,[31][32][33] exhibit true randomness and therefore have been considered for the comparison with SRFG. Another recent algorithm depicted in [18] is also considered for comparison due to its related features.…”

Section: Comparative Analysismentioning

confidence: 99%

A Secure Random Number Generator with Immunity and Propagation Characteristics for Cryptography Functions

et al. 2021

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Cryptographic algorithms and functions should possess some of the important functional requirements such as: non-linearity, resiliency, propagation and immunity. Several previous studies were executed to analyze these characteristics of the cryptographic functions specifically for Boolean and symmetric functions. Randomness is a requirement in present cryptographic algorithms and therefore, Symmetric Random Function Generator (SRFG) has been developed. In this paper, we have analysed SRFG based on propagation feature and immunity. Moreover, NIST recommended statistical suite has been tested on SRFG outputs. The test values show that SRFG possess some of the useful randomness properties for cryptographic applications such as individual frequency in a sequence and block-based frequency, long run of sequences, oscillations from 0 to 1 or vice-versa, patterns of bits, gap bits between two patterns, and overlapping block bits. We also analyze the comparison of SRFG and some existing random number generators. We observe that SRFG is efficient for cryptographic operations in terms of propagation and immunity features.

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“…However, the single Lyapunov index and limited entropy of bernoulli mapping leads to higher cost. In 2019, Hsueh and Chen [66] proposed an ultra-low voltage chaos-based true random number generator for IoT applications. e authors used folded Bernoulli mapping to generate random numbers.…”

Section: Bernoulli Mappingmentioning

confidence: 99%

A Survey on True Random Number Generators Based on Chaos

Tang

et al. 2019

Discrete Dynamics in Nature and Society

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With the rapid development of communication technology and the popularization of network, information security has been highly valued by all walks of life. Random numbers are used in many cryptographic protocols, key management, identity authentication, image encryption, and so on. True random numbers (TRNs) have better randomness and unpredictability in encryption and key than pseudorandom numbers (PRNs). Chaos has good features of sensitive dependence on initial conditions, randomness, periodicity, and reproduction. These demands coincide with the rise of TRNs generating approaches in chaos field. This survey paper intends to provide a systematic review of true random number generators (TRNGs) based on chaos. Firstly, the two kinds of popular chaotic systems for generating TRNs based on chaos, including continuous time chaotic system and discrete time chaotic system are introduced. The main approaches and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review TRNGs based on current-mode chaos for this problem. Finally, quantitative results are given for the described methods in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of TRNGs based on chaos.

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An ultra-low voltage chaos-based true random number generator for IoT applications

Cited by 30 publications

References 21 publications

A lightweight true random number generator using beta radiation for IoT applications

A lightweight true random number generator using beta radiation for IoT applications

A Secure Random Number Generator with Immunity and Propagation Characteristics for Cryptography Functions

A Survey on True Random Number Generators Based on Chaos

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