A high speed truly IC random number source for smart card microcontrollers

Bucci, Marco; Germani, L.; Luzzi, R.; Tommasino, Pasquale; Trifiletti, Alessandro; Varanonuovo, M.

doi:10.1109/icecs.2002.1045378

Cited by 6 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The random numbers are categorized into two types: (1) true random number generator (TRNG), and (2) pseudorandom number generator (PRNG). TRNGs are generally based on physical and natural phenomenon and are non-deterministic, such as quantum random process, photon noise, frequency jitter in the oscillator, thermal noise, human brain signals, and free-running oscillator [16][17][18]. The generated sequences can be passed through the sampling and post-processing techniques to enhance the randomness.…”

Section: Introductionmentioning

confidence: 99%

Pseudorandom Number Generator (PRNG) Design Using Hyper-Chaotic Modified Robust Logistic Map (HC-MRLM)

et al. 2020

View full text Add to dashboard Cite

Robust chaotic systems, due to their inherent properties of mixing, ergodicity, and larger chaotic parameter space, constitute a perfect candidate for cryptography. This paper reports a novel method to generate random numbers using modified robust logistic map (MRLM). The non-smooth probability distribution function of robust logistic map (RLM) trajectories gives an un-even binary distribution in randomness test. To overcome this disadvantage in RLM, control of chaos (CoC) is proposed for smooth probability distribution function of RLM. For testing the proposed design, cryptographic random numbers generated by MRLM were vetted with National Institute of Standards and Technology statistical test suite (NIST 800-22). The results showed that proposed MRLM generates cryptographically secure random numbers (CSPRNG).

show abstract

Section: Introductionmentioning

confidence: 99%

Pseudorandom Number Generator (PRNG) Design Using Hyper-Chaotic Modified Robust Logistic Map (HC-MRLM)

et al. 2020

View full text Add to dashboard Cite

show abstract

“…This situation limits the use of PRNGs [8][9][10][11]. TRNGs, unlike PRNGs, are systems that make use of unpredictable and uncontrollable entropy (noise) sources and generate random numbers [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A new computer-controlled platform for ADC-based true random number generator and its applications

Coşkun¹,

Pehlivan²,

Akgül³

et al. 2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

The basis of encryption techniques is random number generators (RNGs). The application areas of cryptology are increasing in number due to continuously developing technology, so the need for RNGs is increasing rapidly, too. RNGs can be divided into two categories as pseudorandom number generator (PRNGs) and true random number generator (TRNGs). TRNGs are systems that use unpredictable and uncontrollable entropy sources and generate random numbers. During the design of TRNGs, while analog signals belonging to the used entropy sources are being converted to digital data, generally comparators, flip-flops, Schmitt triggers, and ADCs are used. In this study, a computer-controlled new and flexible platform to find the most appropriate system parameters in ADC-based TRNG designs is designed and realized. As a sample application with this new platform, six different TRNGs that use three different outputs of Zhongtang, which is a continuous time chaotic system, as an entropy source are designed. Random number series generated with the six designed TRNGs are put through the NIST800–22 test, which has the internationally highest standards, and they pass all tests. With the help of the new platform designed, ADC-based high-quality TRNGs can be developed fast and also without the need for expertise. The platform has been designed to decide which entropy source and parameter are better by comparing them before complex embedded TRNG designs. In addition, this platform can be used for educational purposes to explain how to work an ADC-based TRNG. That is why it can be utilized as an experiment set in engineering education, as well.

show abstract

An average low offset comparator for 1.25 Gsample/s ADC in 0.18μm CMOS

Stéfanou

Sonkusale

Proceedings of the 2004 11th IEEE International Conference on Electronics, Circuits and Systems, 2004. ICECS 2004.

View full text Add to dashboard Cite

A high speed truly IC random number source for smart card microcontrollers

Cited by 6 publications

References 11 publications

Pseudorandom Number Generator (PRNG) Design Using Hyper-Chaotic Modified Robust Logistic Map (HC-MRLM)

Pseudorandom Number Generator (PRNG) Design Using Hyper-Chaotic Modified Robust Logistic Map (HC-MRLM)

A new computer-controlled platform for ADC-based true random number generator and its applications

An average low offset comparator for 1.25 Gsample/s ADC in 0.18μm CMOS

Contact Info

Product

Resources

About