How RNGs Get Practical Use In Software Solutions

by Chris Smith

If you have ever studied even the most basic levels of coding, there are a few projects that it’s almost guaranteed you did for practice. By tradition, the first was likely the ‘Hello World’ task that’s been the standard for decades, but after that, a random number generator was probably the follow-up. Despite being a ‘beginner’ code, RNGs have a critical place in many different industries and come in many different forms.

Simple and Complex RNGs

The place to find the most standard forms of RNGs is in the vast library of entertainment software. RNGs in entertainment apps can range from very simple, limited generators up to wide-ranging RNGs with extensive testing and moderation used when real money is at stake.

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For an example of the latter in a current industry, look no further than online casinos. Casino games run on the back of rigorously tested RNGs, which are so provably random that their RTPs, or returns to player, can only ever be given as averages. This means the games on a list like Betfair’s best RTP slots are never guaranteed wins, even if those with better RTPs have slightly better odds over a long session. The result is that these games can be confidently stated to be provably fair to all their players.

Data Security

A step above these systems would be those used for data security. The backbone of any data protection system is a robust encryption solution, and all high-level encryption runs on some form of RNG. To put it in the simplest possible way, data that is to be sent is encoded using a key randomly generated by the system, and once it is sent the only way it can be uncoded is by using that same key.

Different encryption algorithms use different exact methods but each follows the same basic principle. The main difference between these RNGs and the ones mentioned before is the scale. The keys generated can be up to 128 characters long or even 256 characters for the most advanced encryption. At that level, you’re looking at encryption used for things like US national security.

“True” RNGs

Beyond even the most complex and intricate RNGs are a category known as True RNGs. The distinction between these and other RNGs, known as pseudorandom number generators, is that these results do not have human interference at any point. Even the most complex programmed system has to have someone make limits for it. True RNGs instead rely on factors with no humans involved.

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To find these factors, we have often looked to nature. Some of the earliest examples used things like nuclear decay as a base, although the most popular option now is atmospheric noise such as Random.org’s service. The complete unpredictability means that results using it as a base are as close to perfectly random as we can get.

This has been a brief overview only, and random number generators have a far greater depth when studied in detail. We encourage you to put in some time and research this underappreciated part of coding.