Using nanoresonators with robust chaos as hardware random number generators

2020 ◽  
Vol 30 (4) ◽  
pp. 043126
Author(s):  
W. G. Dantas ◽  
Lucas R. Rodrigues ◽  
Sebastian Ujevic ◽  
André Gusso
Keyword(s):  
Random Number ◽  
Random Number Generators ◽  
Hardware Random Number Generators

scholarly journals Challenges in Certifying Small-Scale (IoT) Hardware Random Number Generators

2021 ◽  
pp. 165-181
Author(s):  
Darren Hurley-Smith ◽  
Julio Hernandez-Castro
Keyword(s):  
Computer Security ◽  
Random Number ◽  
Statistical Tests ◽  
Statistical Testing ◽  
Small Scale ◽  
Security Systems ◽  
Random Number Generators ◽  
Iot Devices ◽  
Computational Resources ◽  
Hardware Random Number Generators

AbstractThis chapter focuses on the testing and certification of Random Number Generators (RNG). Statistical testing is required to identify whether sequences produced by RNG demonstrate non-random characteristics. These can include structures within their output, repetition of sequences, and any other form of predictability. Certification of computer security systems draws on such evaluations to determine whether a given RNG implementation contributes to a secure, robust security system. Recently, small-scale hardware RNGs have been targeted at IoT devices, especially those requiring security. This, however, introduces new technical challenges; low computational resources for post-processing and evaluation of on-board RNGs being just two examples. Can we rely on the current suite of statistical tests? What other challenges are encountered when evaluating RNG?

scholarly journals TRNGs from Pre-Formed ReRAM Arrays

Cryptography ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 8
Author(s):  
Bertrand Cambou ◽  
Donald Telesca ◽  
Sareh Assiri ◽  
Michael Garrett ◽  
Saloni Jain ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Random Access ◽  
Random Number Generators ◽  
Cryptographic Algorithms ◽  
Cryptographic Keys ◽  
Low Levels ◽  
Stochastic Properties ◽  
Pseudo Random Number Generator ◽  
Number Of Cells

Schemes generating cryptographic keys from arrays of pre-formed Resistive Random Access (ReRAM) cells, called memristors, can also be used for the design of fast true random number generators (TRNG’s) of exceptional quality, while consuming low levels of electric power. Natural randomness is formed in the large stochastic cell-to-cell variations in resistance values at low injected currents in the pre-formed range. The proposed TRNG scheme can be designed with three interconnected blocks: (i) a pseudo-random number generator that acts as an extended output function to generate a stream of addresses pointing randomly at the array of ReRAM cells; (ii) a method to read the resistance values of these cells with a low injected current, and to convert the values into a stream of random bits; and, if needed, (iii) a method to further enhance the randomness of this stream such as mathematical, Boolean, and cryptographic algorithms. The natural stochastic properties of the ReRAM cells in the pre-forming range, at low currents, have been analyzed and demonstrated by measuring a statistically significant number of cells. Various implementations of the TRNGs with ReRAM arrays are presented in this paper.

scholarly journals Design of True Random Number Circuit with Controllable Frequency

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1517
Author(s):  
Xinsheng Wang ◽  
Xiyue Wang
Keyword(s):  
Random Number ◽  
Noise Source ◽  
State Of The Art ◽  
Building Blocks ◽  
Encryption Algorithm ◽  
Random Numbers ◽  
Random Number Generators ◽  
Random Telegraph Noise ◽  
Telegraph Noise ◽  
Novel Method

True random number generators (TRNGs) have been a research hotspot due to secure encryption algorithm requirements. Therefore, such circuits are necessary building blocks in state-of-the-art security controllers. In this paper, a TRNG based on random telegraph noise (RTN) with a controllable rate is proposed. A novel method of noise array circuits is presented, which consists of digital decoder circuits and RTN noise circuits. The frequency of generating random numbers is controlled by the speed of selecting different gating signals. The results of simulation show that the array circuits consist of 64 noise source circuits that can generate random numbers by a frequency from 1 kHz to 16 kHz.

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