Design and implementation of true random number generators based on semiconductor superlattice chaos

2021 ◽  
pp. 105119
Author(s):  
Han Wu ◽  
Zhizhen Yin ◽  
Jianguo Xie ◽  
Peng Ding ◽  
Peihua Liu ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generators ◽  
Semiconductor Superlattice ◽  
Design And Implementation

scholarly journals Min-Entropy Estimation for Semiconductor Superlattice True Random Number Generators

2021 ◽  
Author(s):  
Jing Liu ◽  
Jianguo Xie ◽  
Lu Chao ◽  
Han Wu ◽  
Peng Ding ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Estimation Method ◽  
Random Model ◽  
Security Level ◽  
Minimum Entropy ◽  
Random Number Generators ◽  
Entropy Estimation ◽  
Average Value ◽  
Semiconductor Superlattice

Abstract Semiconductor superlattice true random number generator (SSL-TRNG) has an outstanding practical property to serve as high-throughput and high-security cryptographic applications. Security in random number generators is closely related to the min-entropy of the raw output because feeding cryptographic applications with insufficient entropy leads to poor security and vulnerability to malicious attacks. However, no research has focused on the minimum entropy estimation based on the random model for SSL-TRNG, which is a highly recommended method for evaluating the security of a specific TRNG structure. This paper proposes a min-entropy estimation method for the SSL-TRNG by extending the Markov stochastic model derived from the memory effects. By calculating the boundary of the transition matrix, the min-entropy result is that the average value of each sample (1 bit) is 0.2487. Moreover, we demonstrate that the estimator is accurate enough to adjust compression rate dynamically in post-processing to reach the required security level, estimating entropy on the fly rather than off-line.

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.

scholarly journals Generation of an EDS Key Based on a Graphic Image of a Subject’s Face Using the RC4 Algorithm

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 19
Author(s):  
Alexey Semenkov ◽  
Dmitry Bragin ◽  
Yakov Usoltsev ◽  
Anton Konev ◽  
Evgeny Kostuchenko
Keyword(s):  
Image Recognition ◽  
Random Number ◽  
Statistical Test ◽  
The Other ◽  
Test Results ◽  
Random Generation ◽  
Facial Image ◽  
Random Number Generators ◽  
Graphic Image ◽  
High Level

Modern facial recognition algorithms make it possible to identify system users by their appearance with a high level of accuracy. In such cases, an image of the user’s face is converted to parameters that later are used in a recognition process. On the other hand, the obtained parameters can be used as data for pseudo-random number generators. However, the closeness of the sequence generated by such a generator to a truly random one is questionable. This paper proposes a system which is able to authenticate users by their face, and generate pseudo-random values based on the facial image that will later serve to generate an encryption key. The generator of a random value was tested with the NIST Statistical Test Suite. The subsystem of image recognition was also tested under various conditions of taking the image. The test results of the random value generator show a satisfactory level of randomness, i.e., an average of 0.47 random generation (NIST test), with 95% accuracy of the system as a whole.

Sign in / Sign up

Export Citation Format

Share Document