RNGs Random number generation (RNG) lets computers make random numbers. And RNGs are used everywhere. RNG uses algorithms that use unpredictable physical events to produce randomness. This article explains how different RNGs work and make random bits.
Pseudorandom vs True Random Number Generators
There are two types of random number generators: Pseudorandom number generators (PRNGs) use math to make number sequences that look random.
But they’re not really random. PRNGs will produce the same “random” number sequence if set up the same way. PRNG algorithms use complex math to spread the initial randomness across a long sequence.
Some common PRNG algorithms are linear congruential generators, Mersenne Twisters, and cryptographic PRNGs.
A benefit of PRNGs is reproducibility. You can recreate the same random sequence by reusing the seed. This makes PRNGs useful for things like simulations where controlled randomness is needed.
But again, PRNG output is not truly random. True random number generators (TRNGs) get randomness from physical processes outside the computer.
Some examples are atmospheric noise, circuit heat changes, and quantum effects. The random bits from these physical processes are fundamentally unpredictable and random.
TRNGs don’t need a seed and make different random sequences each time. This makes TRNGs better for things like encryption and gambling where unpredictability is important.
TRNGs need special hardware to connect to these physical sources of entropy.
RNGs at Online Casinos
Online casinos use RNGs a lot to ensure fair gameplay across all games. Game results like slot spins, poker hands and blackjack deals are determined by long random number strings produced in real-time.
Using certified high quality RNGs is key for casinos to prove gameplay is truly random and unbiased. Most big online casinos like www.ignitioncasino.eu use TRNGs to add verifiable randomness to game outcomes.
Hardware TRNGs using physical phenomena like quantum effects or diode noise are seen as sufficiently unpredictable for fair gambling.
The raw random bits from the TRNG get further processed cryptographically to make the final random numbers used in the www.ignitioncasino.eu games.
This processing hides any statistical biases and makes the algorithm irreversible. However, the random numbers alone are not enough.
It’s also important how the games use RNGs. Game developers include RNGs in game math in a way that turns raw randomness into balanced game results.
For example, the RNG may control reel stops in slots or deal cards in blackjack. Good practices include seeding each round uniquely, not using static seeds and applying randomness across the game.
RNG testing and certification by independent auditors is another verification process. Auditors check the random number distribution, unpredictability and other statistical measures of the casino RNG system.
These certifications give transparency to the casino RNGs. In general, the use of high quality audited TRNGs means players can trust online casino games are truly random. The entropy sources and game math are a sure guarantee of fair odds.
Types of Pseudorandom Number Generators
While true RNGs are better for many uses, PRNGs have their place when reproducibility is needed. There are several major types of PRNGs.
Linear congruential generators (LCGs) were the first PRNG algorithms.
LCGs use a simple math formula to generate each new number. However, LCGs have big statistical problems. Their output can be detected unless very large modulus values are used.
Mersenne twisters improve on LCGs by scrambling the output to hide patterns. They have excellent statistical properties and very long cycles. This makes them popular general PRNGs. But they require lots of memory and computations.
Cryptographic PRNGs (CSPRNGs) are designed to look random but be reproducible if you know the seed. This is done by hashing the seed with crypto algorithms over and over.
CSPRNGs can generate random data for crypto uses but their output is not truly random. There are also specialized PRNGs like cellular automata generators and lagged Fibonacci generators.
These have their advantages but are less used than LCGs, Mersenne twisters and CSPRNGs. Choosing the right PRNG is a balance of randomness, reproducibility, speed and memory needs. PRNGs are always short of TRNGs for true unpredictability.
Entropy Sources for True Random Number Generation
While PRNGs use algorithms, TRNGs get output from physically unpredictable processes. Some entropy sources for quality TRNGs are:
Atmospheric noise from radio static or gas discharge tubes can be measured and digitized. Note that the signal needs to be filtered to remove biases.
Quantum mechanics effects like photon paths have inherent randomness that can be sampled. Quantum TRNGs need special hardware but produce ultra-high entropy bits.
Ring oscillators use fluctuating inverter voltages. The jitter between the oscillating signals adds entropy that can be digitized. However, oscillator circuits need to be designed and noise managed.
Diode noise extracts random bits from semiconductor thermal and shot noise. Amplifying and sampling a reverse-biased diode produces solid-state entropy. However, the low bitrate needs entropy pooling from multiple diodes.
There are also other unique ways like radioactive decay times or weather variability. In general, many processes have fine-grained physical randomness that can produce entropy bits.
Using multiple TRNG sources is a great way to get high quality randomness for crypto and security uses that need unpredictable numbers.
Evaluating Random Number Generator Quality
Stringent statistical testing is required to validate the quality and unpredictability of an RNG’s output. Important criteria to test an RNG are:
- Uniform distribution – 0s and 1s should be equally probable with no bias.
- Independence – each bit should be uncorrelated with previous bits.
- Period length – PRNGs should have a long period before repeating.
- Entropy rate – TRNGs should produce high entropy bits per second.
- Cryptographic compliance – CSPRNGs should pass crypto tests.
- Seed isolation – many seeds should produce independent streams for key generation.
These tests need a huge amount of RNG output, often gigabytes of data to validate the statistics. Certified RNGs will pass all tests to give you true randomness where you need it.
