Random Standards Meaning

What is Random Number Generation?

Before diving into random standards, let’s understand the concept of Random Number Generation (RNG). Computers are naturally deterministic, which means that they will produce the same output when fed the same input. Therefore, getting them to generate values that are truly random is difficult. Random number generators (RNGs) are employed to address this challenge.

Random number generation is the process of generating a series of numbers or data points that cannot be reasonably guessed or predicted. The values are used to introduce randomness and unpredictability in computer systems and applications. These random numbers are important for various applications, such as producing cryptographic key pairs, simulations, scientific research, gaming, and random sampling in statistical analysis.

How Can True Randomness Be Achieved?

Why is achieving true randomness important? Cryptocurrency wallets rely on random inputs to calculate recovery phrases for private keys. If the original numbers used for this calculation are predictable, then the resulting private keys will likely be guessable or the RNG will generate the same output more than once. This could jeopardize the security of your digital assets.

Since computers inherently follow instructions, the challenge lies in proving that the generated values achieve true randomness and cannot be deciphered. To put this into context, true randomness means “if I run this thing a billion times, is it truly random?” If yes, it should be devoid of any discernible pattern to foresight yet evenly distributed. This means that the values within the specified range have equal chances of being the outcome sequence.

Given this challenge, how can true randomness be achieved? 

The quality of randomness in RNGs can be assessed through:

• Entropy sources: To inject true randomness into the random number generation process, RNGs often rely on entropy sources. These are information sources that are fundamentally unpredictable, like electronic noise or pressure. Entropy sources enhance the unguessability and unpredictability of the generated numbers.
• RNG certification and randomness standards tests: Mathematical proof of randomness and rigorous statistical tests, as well as testing for randomness defects, can assess RNGs. If an RNG passes all the statistical tests under different conditions, such as temperature, voltage, and frequency, it is deemed to meet the predefined criteria for randomness. RNGs certified with a randomness standard of AIS-31 are considered to have the highest level of unpredictability, robustness, and entropy.

Adhering to random standards is essential in enhancing the quantity and quality of random numbers, while protecting recovery phrases against predictability.