Whether online or land-based, casinos’ integrity in their games should be their top priority. That means their visitors or players should be assured that the results, especially for most luck-based games, are fair and don’t essentially favor the house. To do that, they have to use various technologies that they can’t control.
What we are talking about are various technologies like the RNG. The Random Number Generator (RNG) is a device used to generate unpredictable results for any activity, and this is commonly used in video games or casino games. For example, RNG is used in matchmaking among players in video games, while for casino games, it is used to generate random numbers as a determining factor for your win, just like in slot machines.
There are two types of Random Number Generators: the pseudo-random number generator (PRNG) and true random number generator. But today, let’s talk about TRNGs and know how they work.
Whatever device generates a combination of random numbers through a physical process is called a True Random Number Generation (TRNG). Such devices are generally based on microscopic phenomena generating low-level, statistically random “noise” signals, which involve different quantum phenomena like beam splitter. TRNG is usually put in semiconductors to secure data communications, data storage, and other electronic transactions.
These processes are completely unpredictable as long as the equation controlling this phenomenon is incomputable or unknown. That’s also why TRNGs are used in noise generation, statistical sampling, and communication protocol timers.
The difference between the True Random Number Generator and Pseudo-number Generator is as simple as TRNG uses unpredictable physical processes in generating numbers. At the same time, PRNG is entirely computer-generated and usually uses mathematical algorithms.
The physical processes are like the traditional methods, such as coin flipping, dice throw, and roulette wheels. A physical random number generator can be a random atomic or subatomic phenomenon, following mainly the principle of the laws of quantum mechanics.
The computation methods of the PRNGs are mathematical algorithms that can automatically produce a long set of numbers. While this produces good random properties, the downside is that the sequence may eventually repeat, or memory usage may increase.
Among the two, TRNGs are believed to be the most useful. That’s because some statistical analyses point out that while TRNGs are in no doubt random, Pseudo-RNGs may potentially produce numbers that are predetermined, so they may fail to provide unpredictable results.
You may wonder how true that TRNGs work randomly and that it generates unpredictable results; well, here are some explanations:
True randomness, although very effective, is quite hard to achieve. For random numbers to be generated, the computer will measure some “noise” or some physical phenomenon from the outside of the computer. An example of this is how computers measure an atom’s radioactive decay. Based on quantum theory, the occurrence of radioactive decay is inherently unpredictable, which therefore results in the unpredictability of the numbers generated by TRNGs.
For the process, a well-constructed TRNG may need to collect an entropy through some random process (such as from the noise directly coming from the current flowing in a transistor, or as mentioned earlier, from the radioactive decay), and then set up the entropy signal to remove biases and cleanup or whiten the spectrum of the sequence results.
Several factors can control this process; these include the operating temperature, voltage variation, aging, operating frequency range, and possible electronic noise and upset. These factors must be highly controlled so that the TRNG circuit will not be modified by outsiders who may try to influence the process.
Here’s another example of how your computer may use atmospheric noise in generating random data: your computer may rely on the very precise moment you pressed the keys on your keyboard as its primary source of unpredictable data or entropy. Let’s say your computer detected that you pressed some keys exactly at 0.35434634 seconds after 6 in the evening, or any time associated with the said key presses, you’ll already have a source of entropy that can be used in generating a true random number. That’s just how it simply works. A person cannot be a predictable machine; therefore, any attackers wouldn’t be able to guess the very moment one pressed the keys.
Those are just some important things you need to know about True Random Number Generators (RNG). Again, it’s one of the two types of Random Number Generators, and Pseudo-number Generator is the other. While they both aim to generate random numbers, they differ in the process. RNGs are mainly used among processes like a computer simulation, cryptography, statistical sampling, especially in gambling.