Hybrid Random Number Generator (HRNG)
radom number generator

How good are you at predicting the outcome of rolling a die? ‘Probably not very good, and even with a 30 cubical die, any outcome has a 1-in-30 chance. While it’s frustrating to predict the outcome of simple dice rolls, we know that we can see a number repeat if we roll the dice just a few more times. A cubical die is a random number generator. It’s a physical random number generator that will give us a number between 1 and 30. Other simple random number generators in our everyday lives are drawing from a deck of cards and coin flipping (a really simple one for sure).

Still, with all these and other similar methods, we have two special situations. The first is that it’s not entirely unreasonable to guess the outcome beforehand. This is handy if you’re betting on the outcome, and the odds are higher the more possible outcomes there are (one in two for the coin toss, one in six for the die, one in fifty two for the cards). The second is that the outcomes repeat fairly regularly. Again, they repeat less frequently on average, as the number of outcomes increases.

So what if we want to generate random numbers that are next to impossible to guess and that never repeat? Why would we want to do something like that?

Applications of Random Number Generators
  • Password
  • 5G
  • Wireless communication  /WiFi
  • V2V-Vehicle to Vehicle,V2X -Vehicle to everything
  • A.I Artificial Intelligence
  • IoT-Internet of Things /IIoT-Industrial Internet of Things
  • PGP ,End-to-end encryption
  • Real-time Instant messaging (IM)
  • Cloud Security
  • Blockchain
  • Post- Quantum Cryptography
  • AES-128, AES-256 ,RSA Encryption
  • Key management
  • Cryptocurrency
  • Tokenization
  • Two Factors / Multi Factors Authentication
  • Digital Payments
  • PIN generation
  • Simulations
  • Gaming and lotteries
  • Hardware Security Module -HSM
  • Satellite Communication
  • GPS
  • Small Station
  • Video




cyber security
cyber security
Classification of Random Number Generators
classification of Random Number Generator


Why random numbers are important?

The security paradigm in modern cryptology has been shifted from ciphers to keys since many ciphers are broken as a result of progressive developments in computer science. In modern cryptology, secrecy is based on keys which are basically random numbers (Fairy dust for unpredictability). A cryptosystem is only as secure as its random number generator.

A failure in the random number generation mechanism can easily become a failure for the overall crypto system. (Achilles Heel Metaphor)

cyber security

Encryption Layer of Cyber Security

Whats is entorpy of random number generator ?

“Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin.” (J. von Neumann, 1951)

An ideal random number generator  is a computational or a physical device which generates a stream of Statistically independent, Identically distributed,Unpredictable,numbers or symbols that create a discrete memoryless information source with positive entropy.

Hybrid entorpy sources of random number generator
Quantum random number generators (QRNG)
  • Shot noise, a quantum mechanical noise source in electronic circuits. A simple example is a lamp shining on a photodiode. Due to the uncertainty principle, arriving photons create noise in the circuit. Collecting the noise for use poses some problems, but this is an especially simple random noise source. However, shot noise energy is not always well distributed throughout the bandwidth of interest. Gas diode and thyratron electron tubes in a crosswise magnetic field can generate substantial noise energy (10 volts or more into high impedance loads) but have a very peaked energy distribution and require careful filtering to achieve flatness across a broad spectrum.
  • A nuclear decay radiation source, detected by a Geiger counter attached to a PC.
  • Photons travelling through a semi-transparent mirror. The mutually exclusive events (reflection/transmission) are detected and associated to ‘0’ or ‘1’ bit values respectively.
  • Amplification of the signal produced on the base of a reverse-biased transistor. The emitter is saturated with electrons and occasionally they will tunnel through the band gap and exit via the base. This signal is then amplified through a few more transistors and the result fed into a Schmitt trigger.
  • Spontaneous parametric down-conversion leading to binary phase state selection in a degenerate optical parametric oscillator.
  • Fluctuations in vacuum energy measured through homodyne detection.[9][third-party source needed]
Deterministic (Pseudo-) random number generators (PRNG)
  • Algorithmic generators
  • Usually faster, with good statistical properties
  • Must be computationally secure, i. e. it should be computationally difficult to guess the next or previous values
  • Their period must be very long
Physical (True-) random number generators (TRNG)
  • Using some physical source of randomness
  • Unpredictable, usually having suboptimal statistical characteristics
  • Usually slower speed


Hybrid random number generators (HRNG)
  • Deterministic random number generator seeded repeatedly by a physical random number generator
  • True random number generator with algorithmic (e. g. cryptographic) post-processing
  • Quantum Random Number Generator


classification of Random Number Generator
classification of Random Number Generator

Encryption Features

Protect Internet of Things (IoT), big data, Cloud Storage and Data Security