Randomness is an important concept in today’s world, and it powers many of the technologies we use. From computer programming to games, randomness ensures unpredictability, which contributes to the security and uniqueness of digital systems.
In ancient times, dice, cards, flipping coins and drawing straws were used to ensure randomness in selection. Today, random number generators are being used to generate a sequence of numbers that are unpredictable. These random numbers are applied in the areas of computer programming, the Internet of Things (IoT), statistical analysis, gaming and cryptography, among others.
Random numbers serve as the backbone for various applications, from ensuring fair play in online games to securing transactions and security systems by guaranteeing unpredictability in the systems. In this article, we’ll explore how random number generators work and how they are being used.
What are random number generators (RNGs)?
An RNG is an algorithm or a hardware device that generates a sequence of numbers with equal probability of selection. This uniform distribution of randomness ensures that each number has a fair chance of appearing, eliminating any predictability.
RNGs are used in various fields, but mainly computer programming to generate random data for simulations, games and cryptography. It is also used on the Internet of Things (IoT) to add randomness to sensor readings and device behaviour. Statistical analysis utilises RNGs to generate random samples for studies and experiments. Other fields like gaming use RNGs to provide unpredictable outcomes in the vast selection of jackpot games available online, such as slots, roulette and card games.
There are two different types of RNGs.
True random number generators (TRNGs)
TRNGs generate random numbers using natural and physical measurements such as thermal noise, atmospheric radiation and radioactive decay. These are called entropy sources because they provide random and unpredictable values. The numbers generated are called true random numbers because their randomness is derived from natural phenomena and physical processes that are truly random.
How TRNGs work
- TRNGs use sensors or detectors to measure physical processes that serve as sources of entropy. They take measurements from the entropy sources in the form of analog signals, which are then converted into digital form.
- To ensure a uniform distribution, TRNGs may further process the inputs using cryptographic algorithms. The final output is a true random number (TRN) that is not deterministic.
With TRNGs, there is a high level of randomness, but they are also very slow because of the processes they undergo to generate numbers. They are commonly used in systems that require high-security protocols.
Pseudo-random number generators (PRNGs)
PRNGs generate a sequence of apparently random numbers using a seed value and an algorithm.
A major challenge with the generation of random numbers using algorithms is that algorithms are designed to be deterministic – that is, they would yield the same output when given the same input.
This is a challenge for RNGs that are meant to generate unpredictable sequences of numbers.
To mitigate this, PRNGs work by applying deterministic algorithms to an initial value called a seed. They ensure that the same value is not generated by randomizing the seed generation using methods such as user interaction.
How PRNGs work
- Seed initialization: PRNGs begin with a seed value, which serves as the starting point for generating pseudo-random numbers. This seed can be generated using time-based seeding, employing user interaction, or based on the previous number generated.
- Algorithmic generation: PRNGs generate a sequence of numbers using the initialized seed. The algorithm used here is designed to ensure a uniform distribution and a lack of discernible patterns.
While the numbers generated by PRNGs may seem random, if the seed value is known, the output can be predicted. PRNGs are faster than TRNGs because they are deterministic. However, a major limitation is their periodicity. They can repeat number sequences after a certain number of iterations. PRNGs are commonly used in computer simulations and code testing.
Conclusion
RNGs are essential in fields such as cryptography, computer simulations, and statistical analysis, as well as in any field where unpredictability is required. They power some of the most advanced technology and security systems in the world. Simulations such as the Monte Carlo simulation, which is used to predict the possible outcomes of uncertain events, are very useful in the field of computing.
By understanding how RNGs work, we can decide on which ones to use based on what our needs are and how best to optimize the technologies that utilize RNGs.
