Random number generation

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Practical applications and uses

Main article: Applications of randomness

Random number generators have applications in gambling, statistical sampling, computer simulation, cryptography, completely randomized design, and other areas where a random number is useful in producing an unpredictable result. flavoured condom

Note that, in general, where unpredictability is paramountuch as in security applicationsardware generators are generally preferred, where feasible, over pseudo-random algorithms. lubricated condom

Random number generators are very useful in developing Monte Carlo method simulations as debugging is facilitated by the ability to run the same sequence of random numbers again by starting from the same random seed. They are also used in cryptography so long as the seed is secret. Sender and receiver can generate the same set of numbers automatically to use as keys. condom factory

The generation of pseudo-random numbers is an important and common task in computer programming. While cryptography and certain numerical algorithms require a very high degree of apparent randomness, many other operations only need a modest amount of unpredictability. Some simple examples might be presenting a user with a "Random Quote of the Day", or determining which way a computer-controlled adversary might move in a computer game. Weaker forms of randomness are also closely associated with hash algorithms and in creating amortized searching and sorting algorithms.

Some applications which appear at first sight to be suitable for randomization are in fact not quite so simple. For instance, a system that 'randomly' selects music tracks for a background music system must only appear to be random, and may even have ways to control the selection of music; a true random system would have no restriction on the same item appearing two or three times in succession.

Online poker sites, such as PokerStars, have said they use a random number generator as their software gaming method, but individual statistics gathered from hundreds of multiple players and tens of thousands of played hands show that the outcome of games are nowhere near the statistical occurences that happen when using standard playing cards in live games. Therefore, the 'randomness' has been called in to question by many players over recent years. Just as the music system that 'appears' to be random yet may have ways to control the selection of music, the random number generator used in online software poker games may also be able to control the outcome of cards which will appear thereby benefiting, almost 'chosing', the winner and loser of each game. Moreover, there is talk that the actual player is 'randomly' chosen by the program to be a pre-determined winner before the games even begin by where they are randomly seated. At this time, that theory has yet to be proven.

"True" random numbers vs. pseudorandom numbers

Main article: Pseudorandom number generator

There are two principal methods used to generate random numbers. One measures some physical phenomenon that is expected to be random and then compensates for possible biases in the measurement process. The other uses computational algorithms that produce long sequences of apparently random results, which are in fact completely determined by a shorter initial value, known as a seed or key. The latter type are often called pseudorandom number generators.

A "random number generator" based solely on deterministic computation cannot be regarded as a "true" random number generator, since its output is inherently predictable. How to distinguish a "true" random number from the output of a pseudo-random number generator is a very difficult problem. However, carefully chosen pseudo-random number generators can be used instead of true random numbers in many applications. Rigorous statistical analysis of the output is often needed to have confidence in the algorithm.

Generation methods

Physical methods

Main article: hardware random number generator

The earliest methods for generating random numbers dice, coin flipping, roulette wheels are still used today, mainly in games and gambling as they tend to be too slow for most applications in statistics and cryptography.

A physical random number generator can be based on an essentially random atomic or subatomic physical phenomenon whose unpredictability can be traced to the laws of quantum mechanics. Sources of entropy include radioactive decay, thermal noise, shot noise, avalanche noise in Zener diodes, clock drift, the timing of actual movements of a hard disk read/write head, and radio noise. However, physical phenomena and tools used to measure them generally feature asymmetries and systematic biases that make their outcomes not uniformly random. A randomness extractor, such as a cryptographic hash function, can be used to obtain uniformly distributed bits from a non-uniformly random source, though at a lower bit rate.

Recently, a team in Bar-Ilan University in Israel has been able to create a physical random bit generator at a 300 Gbit/sec rate, making it the fastest ever.

Various imaginative ways of collecting this entropic information have been devised. One technique is to run a hash function against a frame of a video stream from an unpredictable source. Lavarand used this technique with images of a number of lava lamps. HotBits measures radioactive decay with GM tubes, while Random.org uses variations in the amplitude of atmospheric noise recorded with a normal radio.

Another common entropy source is the behavior of human users of the system. While people are not considered good randomness generators upon request, they generate random behavior quite well in the context of playing mixed strategy games. Some security-related computer software requires the user to make a lengthy series of mouse movements or keyboard inputs to create sufficient entropy needed to generate random keys or to initialize pseudorandom number generators.

Computational methods

Pseudo-random number generators (PRNGs) are algorithms that can automatically create long runs of numbers with good random properties but eventually the sequence repeats (or the memory usage grows without bound). The string of values generated by such algorithms is generally determined by a fixed number called a seed. One of the most common PRNG is the linear congruential generator, which uses the recurrence

to generate numbers. The maximum number of numbers the formula can produce is the modulus, m. To avoid certain non-random properties of a single linear congruential generator, several such random number generators with slightly different values of the multiplier coeffient a can be used in parallel, with a "master" random number generator that selects from among the several different generators.[citation needed]

A simple pen-and-paper method for generating random numbers is the so-called middle square method suggested by John Von Neumann. While simple to implement, its output is of poor quality.

Most computer programming languages include functions or library routines that purport to be random number generators. They are often designed to provide a random byte or word, or a floating point number uniformly distributed between 0 and 1.

Such library functions often have poor statistical properties and some will repeat patterns after only tens of thousands of trials. They are often initialized using a computer's real time clock as the seed. These functions may provide enough randomness for certain tasks (for example video games) but are unsuitable where high-quality randomness is required, such as in cryptographic applications, statistics or numerical analysis. Better pseudo-random number generators such as the Mersenne Twister are widely available. Much higher quality random number sources are available on most operating systems; for example /dev/random on various BSD flavors, Linux, Mac OS X, IRIX, and Solaris, or CryptGenRandom for Microsoft Windows.

An example of a simple pseudo-random number generator that is computationally fast, has good (albeit not cryptographically strong) randomness properties, is given by the following "Multiply-With-Carry" generator of G. Marsaglia (note that this example is not thread safe):

m_w = <choose-initializer>; /* must not be zero */

m_z = <choose-initializer>; /* must not be zero */



uint get_random()

{

m_z = 36969 * (m_z & 65535) + (m_z >> 16);

m_w = 18000 * (m_w & 65535) + (m_w >> 16);

return (m_z << 16) + m_w; /* 32-bit result */

}

Generation from a probability distribution

There are a couple of methods to generate a random number based on a probability density function. These methods involve transforming a uniform random number in some way. Because of this, these methods work equally well in generating both pseudo-random and true random numbers. One method, called the inversion method, involves integrating up to an area greater than or equal to the random number (which should be generated between 0 and 1 for proper distributions). A second method, called the acceptance-rejection method, involves choosing an x and y value and testing whether the function of x is greater than the y value. If it is, the x value is accepted. Otherwise, the x value is rejected and the algorithm tries again.

Post-processing and statistical checks

Even given a source of plausible random numbers (perhaps from a quantum mechanically based hardware generator), obtaining numbers which are completely unbiased takes care. In addition, behavior of these generators often changes with temperature, power supply voltage, the age of the device, or other outside interference. And a software bug in a pseudo-random number routine, or a hardware bug in the hardware it runs on, may be similarly difficult to detect.

Generated random numbers are sometimes subjected to statistical tests before use to ensure that the underlying source is still working, and then post-processed to improve their statistical properties.

See also: Statistical randomness

Other considerations

Random numbers uniformly distributed between 0 and 1 can be used to generate random numbers of any desired distribution by passing them through the inverse cumulative distribution function (CDF) of the desired distribution. Inverse CDFs are also called quantile functions. To generate a pair of statistically independent standard normally distributed random numbers (x, y), one may first generate the polar coordinates (r, ), where r~22 and ~UNIFORM(0,2) (see Box-Muller transform).

Some 0 to 1 RNGs include 0 but exclude 1, while others include or exclude both.

The outputs of multiple independent RNGs can be combined (for example, using a bit-wise XOR operation) to provide a combined RNG at least as good as the best RNG used. This is referred to as software whitening.

Computational and hardware random number generators are sometimes combined to reflect the benefits of both kinds. Computational random number generators can typically generate pseudo-random numbers much faster than physical generators, while physical generators can generate true randomness.

Low-discrepancy sequences as an alternative

Some computations making use of a random number generator can be summarized as the computation of a total or average value, such as the computation of integrals by the Monte Carlo method. For such problems, it may be possible to find a more accurate solution by the use of so-called low-discrepancy sequences, also called quasirandom numbers. Such sequences have a definite pattern that fills in gaps evenly, qualitatively speaking; a truly random sequence may, and usually does, leave larger gaps.

Activities and demonstrations

The SOCR resource pages contain a number of hands-on interactive activities and demonstrations of random number generation using Java applets.

See also

Flipism

Hardware random number generator

List of random number generators

PP (complexity)

Procedural generation

Randomization

Randomized algorithm

Random number generator attack

Random password generator

Randomness

Slot machine

Random.org

References

^ Walker, John. "HotBits: Genuine Random Numbers". http://www.fourmilab.ch/hotbits/. Retrieved 2009-06-27. 

^ Haahr, Mads. "Introduction to Randomness and Random Numbers". http://random.org/randomness/. Retrieved 2009-04-08. 

^ Halprin, Ran; Naor, Moni (PDF). Games for Extracting Randomness. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science. http://www.neko.co.il/games4rand.pdf. Retrieved 2009-06-27.  Main site

^ TrueCrypt Foundation. "TrueCrypt Beginner's Tutorial, Part 3". http://www.truecrypt.org/docs/?s=tutorial3. Retrieved 2009-06-27. 

^ Marsaglia, George (1999-01-12). "sci.stat.math". http://groups.google.com/group/sci.crypt/browse_thread/thread/ca8682a4658a124d/. Retrieved 2010-02-10. 

^ The MathWorks. "Common generation methods". http://www.mathworks.com/access/helpdesk/help/toolbox/stats/index.html?/access/helpdesk/help/toolbox/stats/bqttfc1.html. Retrieved 2009-01-13. 

External links

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