Random number generator

In summary: Some CSPRNGs are based on hash functions, others on block ciphers, and still others may be based on elliptic curves.In summary, a random number generator can work either by measuring something physical or by performing a series of computations. These generators can be used in a variety of applications, such as in simulations, cryptography, and games. It is important to use a cryptographically secure random number generator when strong randomness is required.
  • #1
shahrzad1994
4
0
How does a random number generator work ?
What is the usage of it ?
 
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  • #3
phion said:
I don't want to use it right now,I need to know how does it work ? Is there different ways to arrive a number o there is only one way ? And where can we use random number generator ?
 
  • #4
shahrzad1994 said:
I don't want to use it right now,I need to know how does it work ? Is there different ways to arrive a number o there is only one way ? And where can we use random number generator ?

A "true" random number generator (RNG or TRNG) works by measuring something physical which is theorized to behave randomly (such as radioactive decay) or something which is so complicated that it is sure to behave randomly (such as thermal noise). Any number of sources or measuring techniques can be used. Post-processing can be used to improve the quality of poor sources.

A "pseudo" random number generator (PRNG) works by having a computer perform a series of arithmetic or logical computation that results in what appears, at first glance, to be just a list of jumbled up numbers. This can be something as simple as a multiplicative-congruential generator, a linear feedback shift register or something more complicated. Typically a PRNG will have a "seed" -- an initial value that determines how the stream will start and an internal state that evolves as each pseudo-random number is emitted. The size of the internal state constrains how long it will take before the output stream begins to repeat itself.

A cryptographically secure random number generator (CSPRNG) is a pseudo random number generator which is good enough that an opponent with bounded computer power is unable to distinguish its output from a truly random stream. There are a variety of approaches that can be used. Multiplicative congruential and linear feedback shift registers are too easy to crack and are not cryptographically secure.
 
  • #5


A random number generator is a mathematical algorithm or device that produces a sequence of numbers that have no pattern or predictability. It works by using a seed value, which is an initial value that is input into the algorithm. This seed value is then used to generate the next number in the sequence, and this process is repeated to produce a series of random numbers.

The usage of a random number generator varies depending on the field of study or industry. In science and research, random number generators are used in experiments and simulations to introduce an element of randomness and control for bias. In computer programming, they are used to generate unique identifiers or to simulate random events in games or simulations. In cryptography, random number generators are used to create encryption keys and ensure the security of data. Additionally, random number generators are also used in gambling and lottery systems to ensure fairness and randomness in the selection of numbers. Overall, random number generators play a crucial role in many fields and industries where true randomness is needed.
 

1. What is a random number generator?

A random number generator is a computer program or algorithm that generates numbers or sequences of numbers that appear to be random. These numbers are not truly random, but their generation is based on a complex mathematical formula and can be used for various purposes, such as creating unique identifiers, encryption, and statistical analysis.

2. How does a random number generator work?

A random number generator works by using a mathematical algorithm to generate a sequence of numbers that have no predictable pattern. The algorithm takes a starting point (called a "seed") and uses it to generate a new number. This process is repeated multiple times, resulting in a sequence of numbers that appear random.

3. Are all random number generators the same?

No, there are different types of random number generators. Some use physical sources, such as atmospheric noise or radioactive decay, to generate random numbers. Others use mathematical algorithms, and the quality of the randomness depends on the complexity of the algorithm. It is important to choose a random number generator that is appropriate for the intended use.

4. Can a random number generator produce the same number twice?

Technically, yes, a random number generator can produce the same number twice. However, the chance of this happening is extremely low, especially if the algorithm used is complex and the seed is constantly changing. The more times the generator is used, the lower the chances of producing the same number twice.

5. Is a random number generator truly random?

No, a random number generator is not truly random. This is because the numbers are generated using a specific algorithm and are not based on true randomness. However, for most practical purposes, the numbers appear random and can serve the intended purpose effectively.

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