The concept of randomness has long been a topic that philosophers, scientists, statisticians, and other non-specialists have pondered. Different ideas have been put forward about what it means for data to be random and about the nature of probability. For example, the movements of the planets initially seemed random and accidental, but early astronomers were able to make predictions by discovering a pattern in the planets.
Random, the concept of randomness, can be used to mean unpredictability, randomness, and uncertainty. In computer science, it is often used to refer to operations that are associated with randomness. True randomness is considered an elusive property because computers perform deterministic operations. Therefore, in most cases, random functions in programming languages generate pseudorandom numbers.
Pseudorandom numbers are not completely random but behave like random numbers. Pseudorandom numbers represent numbers that are not easily correlated when analyzed. They are generated by the number generator and are based on a seed value that is used as a starting point.
Pseudorandom numbers are used in many applications. For instance, in game development, cryptography, simulations, statistical analysis, and testing. However, for security-critical applications, it is recommended to choose hardware or physical sources that generate true random numbers.
How Is Pseudorandom Generated?
Pseudorandom is a concept used in computer science. Computers don't generate truly random numbers because the algorithms involved in computers are deterministic processes. Pseudorandom number generation allows these deterministic algorithms to work with a seed value as a starting point. Given the initial seed value, the numbers generated by the algorithm follow one after another, and the successive numbers appear to be independent and random.
What Is a Pseudorandom Generator (PRG) Application?
The pseudorandom generator used today for most operating systems and various programming languages exhibits similar behavior. A pseudorandom generator is usually found in systems such as Linux or in related programming language libraries. The logic of PRGs usually involves the following steps:
In the first step, the pseudorandom generator sets a starting point or seed value. The seed value initializes the internal state of the pseudorandom generator. This value can often be based on information from the system clock, timestamps, or other random sources. Then, while the generator is running, the internal state is continuously modified and shuffled. It affects the number of pseudorandoms to be generated. The shuffling process uses the internal state to produce output sequences that are as random as possible and can be based on a variety of external and random input data. For instance, factors such as a user's keyboard and mouse usage, network traffic, hardware outages, and other external types can be used in this shuffling process.
The internal state of the pseudorandom generator is constantly changing, and these changes are performed to provide the best possible protection against a statistical analysis of the generated output data.
How to Assess Pseudorandom Generator (PRG) Output Quality?
Many standards define the requirements of a pseudorandom generator.
Various statistical tests can be used to evaluate the quality of a pseudorandom generator. These tests are applied to check the randomness and predictability of the generated sequences. Some example statistical tests are:
- Bit count test
- Double 00 number test
- Longest sequence test
Bit Count Test
The number of 1 bits in the generated sequence should be close to the number of 0 bits. That is, the bits are expected to be approximately equally distributed.
Double 00 Number Test
The number of consecutive 00 pairs in the generated sequence must equal one quarter or less of all bits. It indicates that there is no irregularity or pattern.
Longest Sequence Test
The length of the longest sequence of zeros or ones in the generated sequence should be similar to mathematical predictions. That is, it should show random lengths, rather than a predictable structure.
These statistical tests are used to evaluate the randomness properties of the pseudorandom generator's output sequences. These tests are important to ensure that the generator meets the desired randomness standards and to obtain reliable results in applications such as security or statistical analysis.
What Are the Differences Between Random and Pseudorandom?
The terms random and pseudorandom refer to different meanings associated with randomness. Random refers to true randomness. It indicates a situation where an event or an outcome is truly random. Random refers to a situation that is completely unpredictable, uncertain, and shows no order or predictability. For instance, when a die is thrown, it is random which number the die will show.
Pseudorandom refers to a set of data generated using mathematical algorithms and an initial value. Pseudorandom numbers may lack true randomness but exhibit a desired level of randomness-like behavior. In summary, random refers to true randomness, while pseudorandom behaves similarly to the concept of randomness.
