Random Number Generator (RNG)

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What is a Random Number Generator (RNG)?

A random number generator (RNG) is a type of algorithm or hardware device that can produce a series of arbitrary numbers. The quality of an RNG is often measured by its ability to produce unpredictable sequences with a high degree of randomness (entropy).

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There are two main types of RNGs: pseudorandom number generators (PRNGs) and true random number generators (TRNGs).

  • PRNGs use mathematical calculations to generate sequences that seem to be random because numerical patterns are difficult to detect.
  • TRNGs use unpredictable physical phenomena or quantum effects to generate genuinely random number sequences.

RNG form factors vary depending on their specific implementation and purpose. While some may resemble USB thumb drives, others may be integrated into larger systems as PCIe cards, embedded in microcontrollers, or designed as standalone devices with their own power supplies and interfaces.

What is a Random Number Generator (RNG)?

5 Key Takeaways

  • Random number generators play an important role in statistical sampling, scientific and engineering simulations, cryptography, gaming, and gambling.
  • There are two main types of random number generators: PRNGs and TRNGs.
  • PRNGs use algorithms and an initial seed value chosen by the developer to generate a series of numbers that statistically resemble random numbers.
  • TRNGs use physical phenomena or quantum effects to generate truly random numbers.
  • Most RNGs in use today are PRNGs whose outputs can pass statistical tests for randomness.

History of RNGs

Throughout history, random number generation has been tightly coupled with technology.

The invention of electronic computers in the mid-20th century introduced the first PRNG, and by the turn of the century, advances in electronics and quantum mechanics led to the creation of TRNGs.

Pre-1880s1880s - 1940s1950s - 1960s1970s - 1980s1990s - 2000s2000s - Present

Physical devices like dice were used as random number generators (RNGs).

RNGs include mechanical devices like lottery ball machines and number picker wheels.

Computers enable the creation of pseudorandom number generators (PRNGs).

Hardware-based random number generators (HRNGs) that can measure physical phenomena and convert them into data enable true random number generators (TRNGs).

Advances in computer science led to more sophisticated PRNGs.

Advances in quantum mechanics are being used to develop more secure TRNGs.

How Random Number Generator Works

There are two main types of RNGs: pseudorandom number generators and true random number generators.

Pseudorandom number generators
Use mathematical algorithms to generate sequences of numbers that mimic randomness. PRNG algorithms process an initial variable (called a seed) through iterative calculations to produce a sequence of numbers that can pass statistical tests for randomness.

PRNGs are commonly used for tabletop and online gaming and statistical sampling. Given the same seed, a PRNG will always produce the same sequence of numbers. This determinism is useful for debugging computer science and computer engineering simulations. 

True random number generators
Rely on data gathered about physical phenomena to generate a series of random numbers. Since the randomness comes from unpredictable variables, the numbers generated by a TRNG are truly arbitrary, and the same sequence cannot be reproduced.

TRNG use cases include generating sophisticated encryption keys, digital signatures, and internet key exchange protocols. Common data sources include atmospheric noise and radioactive decay.

How Random Number Generator Works

Methods for Generating Random Numbers

There are three basic ways to generate random numbers: TRNG hardware methods, PRNG computational methods, and hybrid methods. The choice of method depends on the specific application and the desired level of randomness.

  • Hardware methods capture unpredictable physical phenomena and use the data to generate random numbers. This type of method can be very secure, but it can also be slow and require special hardware components.
  • Computational methods are simpler and less expensive, but they can only generate pseudorandom numbers. This method is used in games, gambling, and simulations, but its use is limited to high-security scenarios.
  • Hybrid methods combine the benefits of both hardware and computational methods. Typically, a hardware method is used to provide unpredictable seeds to a computational method in order to enhance the randomness of the generated sequence.

Example Algorithm for Pseudo-Random Number Generator

The Mersenne Twister is one of the most popular pseudorandom number generator algorithms because there is a very long period before it repeats number sequences.

The Mersenne Twister is widely used in Monte Carlo method simulations and other applications where a high degree of randomness is required, but security is not the primary concern.

RNG in Gaming

Random number generators are often used in video and tabletop games to provide elements of chance for loot drops, critical hits, card draws, and game movement.

RNG should be used thoughtfully in game development because too much randomness can make the game feel unfair, while too little can make gameplay predictable and replayability boring.

RNG in Gambling

In gambling, RNG ensures fair and unpredictable outcomes in games that rely on statistical probability. This includes offline and online casino games like slots, as well as offline and online card games like poker.

RNGs ensure that each game round is independent of the previous one and all players have a fair chance of winning. In this use case, best practices include rigorous testing and certification by independent third parties, transparent disclosure of RNG implementation details, and regular audits to ensure the integrity and fairness of the system.

The Bottom Line

To understand the random number generator meaning, it’s important to distinguish between pseudorandom number generators and true random number generators. They each use different methods for generating numbers, and they each have different use cases.

PRNGs use deterministic algorithms to produce long number sequences that appear to be random. They are commonly used in applications where the degree of randomness is important, but security is not the primary concern.

TRNGs generate truly random numbers based on physical processes that are inherently unpredictable. TRNGs are used in high-security applications like generating cryptographic keys or generating random nonces and initialization vectors for secure communications.

FAQs

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Margaret Rouse
Senior Editor
Margaret Rouse
Senior Editor

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.