Random Number Generators are an important part of the computing industry, from encryption to online gambling. In this article, gambling developers from AllSpinsWin will discuss everything about one of the most important tools in any online casino operator’s arsenal.
3 Ways for Implementation — Exploit-exercises Recommendations
Random number generators are a necessity in today’s digital age, where the proliferation of computers and data have resulted in many computer-generated simulations. The most common types of random number generators (RNGs) produce values that cannot be predicted and thus offer true security to their users.
Today there are three ways to implement an RNG in code: as a stateful object, as a (stateless) function, or as a (stateless) “splittable PRNG.”
Random Number Generation Basics
Random number generation (RNG) is the process of producing random numbers. It is exploited in cryptography, statistics analysis, and online casino games.
The term “random” has two meanings: it refers to the way in which something occurs or happens at random; but also implies that there isn’t any pattern or order behind how something happens in our world (i.e., there is no cause-and-effect relationship). In this context, “random” means that each possible outcome has equal probability — that is, every number has an equal chance of appearing exactly once out of all possible outcomes when we roll dice or flip coins into a cup every day!
The ABCs of Random NG
The most common type of random number generator, or RNG, is an LCG. It’s often found in games that need a true-random number. A PRNG may also be used to generate a random sequence but it doesn’t produce the same results every time. The HMAC is another type of RNG and it uses a hash function to generate the final output with seed data as input.
Pseudo-Random NG
A PRNG is a device that generates a sequence of numbers that appear to be random. A PRNG is deterministic, so it will always produce the same sequence of numbers if you start with the same seed value.
PRNGs are useful for simulations, but not for security applications like cryptography or key generation; they do not provide any guarantee against collisions in their output space (which means they can still generate duplicate outputs). However, this problem doesn’t affect all implementations of PRNGs — some machines use hash functions instead.
Hardware/True Random NG
Random numbers are generated from a physical process, like radioactive decay. HRNGs use such processes to generate bits that cannot be predicted or reproduced by any other means. They are used in many security applications including data encryption and digital signatures because they help ensure the information is secure. The randomness of the number can be tested using statistical tests for randomness to determine if it was actually produced by an HRNG. There have been recent developments in this area with new standards and features that will improve the process.
Statistical Independence
If we pick two random values from our sample and then add them together, we would not expect to get any exact number for the result. Instead, there will be an average increase or decrease in value depending on how far apart those original numbers were chosen.
The same thing happens when we generate a new number at random; it’s not determined by any particular property of your computer or other device (like its clock speed), but instead depends on what else is going on at that particular moment in time.
Predictability
PRNGs are deterministic, which means they produce the same output every time they’re run. HRNGs are not deterministic but require more memory to store their output, so they’re typically slower and less efficient than their counterparts.
Periodicity
It’s the length of time between successive occurrences of an event. In other words, how many times something happens during any given period (e.g. every second), to call it ‘periodic’ behavior.
GOOD and BAD Random Numbers
Before we dive into the details, it’s important to distinguish good and bad random numbers. A good random number is unpredictable; it has no correlation between any two runs. A bad random number is predictable; it exhibits a strong correlation between the numbers generated in consecutive runs of the same algorithm (e.g., if you run an algorithm twice, there’s a high probability that the two sets of numbers will be similar).
A uniform distribution means that each value in your integer range has an equal chance at being chosen as part of your set. For example, with a 32-bit integer range from 0 through 4294967295 (2^31–1), there are only 5 bits left over to choose integers beyond 32767! So each possible value will get one chance out of five for being picked — or about 3% chance per bit!
In contrast, non-uniform distributions occur when there aren’t enough bits left over after choosing all possible values from above such as when using a 64-bit integer range like 2^64 – 1 + 1 = 2147483647 or even larger ranges depending on your machine needs.
Conclusion
As we’ve seen, PRNG and HRNG (TRNG) work differently. You should be aware of the difference when choosing between them.