# random number generator

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## random number generator

[′ran·dəm ′nəm·bər ‚jen·ə‚rād·ər]
(computer science)
A mathematical program which generates a set of numbers which pass a randomness test.
An analog device that generates a randomly fluctuating variable, and usually operates from an electrical noise source.

## Random Number Generator

a device for the generation of random numbers that are uniformly distributed over a given range of numbers. Its uses include the simulation of the actual operating conditions of automatic control systems, the solution of problems by the Monte Carlo method, and the modeling of random changes of production parameters in automated control systems. Besides their direct use in statistical models, the uniformly distributed random numbers produced by a random number generator can be used to form number sequences having a specified distribution law.

The principal unit in a random number generator is a generator of equally probable random digits, from which the required multidigit combinations of digits, or numbers, are formed. Most generators produce binary digits. Various primary sources of random signals are used in random digit generators—for example, the intrinsic noise of special resistors or of electron-tube, gas-discharge, or semi-conductor devices. Other sources of random signals include α –, β–, and γ-radioactivity and fluctuations in the phase and amplitude of harmonic oscillations. The random digit generator contains an appropriate device for shaping the initial signals, which is called the source of the primary stochastic process. The generator also includes the following: a shaping amplifier, which converts the initial stochastic process into a form that is convenient for digital interpretation; an analog-to-digital converter of the shaped random signals into discrete equally probable states of some electronic device (such as a flip-flop), where each state corresponds to a certain digit; and a probability stabilizer, which ensures the stability of the probability characteristics of the generated sequence of digits.

One common stabilization method is based on a combination of direct and inverted representations of the generated digits. In this case, the stabilized sequence S1, S2, …, Si … is formed from the basic sequence ξ12 … ξi, … and the control sequence y1, y2, …, yi, … according to the rule

Depending on the method of forming the multidigit random numbers from the elementary sequences of equally probably digits, random number generators are divided into sequential and parallel types; a composite type making use of both methods is also distinguished. The sequential type has only one random digit generator. In this case, an n-position random number (that is, a number with n digits) is formed by filling in turn each position of the corresponding register. In the parallel type, each digit of the number being formed has its own generator, and all the digits are entered simultaneously in the register. The parallel type generates random numbers more quickly but requires more complicated equipment than does the sequential type. This disadvantage, however, may not be important when integrated circuits are used.

### REFERENCES

Bobnev, M. P. Generirovanie sluchainykh signalov, 2nd ed. Moscow, 1971.
Iakovlev, V. V., and R. F. Fedorov. Stokhasticheskie vychislitel’nye mashiny. Leningrad, 1974.

I. A. DANIL’CHENKO

## random number generator

A software routine that produces a random number. Used in applications such as computer games and cryptographic key generation, random numbers are easily created in a computer due to many random events that take place. For example, only the difference of a few milliseconds between keystrokes is enough to seed a random number generation routine with a different starting number each time.

Once seeded, an algorithm computes different numbers throughout the session. The numbers that are created must be distributed evenly over a certain range, and they cannot be predictable (the next number cannot be determined from the last).
References in periodicals archive ?
PRNG and chaos-based random number generators are used with TRNG in duplicate structures [14], [15].
Comparing this data with similar plots that highlight the difference between different algorithms, we see that the influence of the random number generator dwarfs many of the algorithmic differences.
If the value of c is taken to be zero, the resulting generator is called a multiplicative linear congruential random number generator (MLCG).
Therefore, these results suggest that there may be issues with the numerical accuracy and the goodness of the random number generator in Excel, but they are not a full scientific assessment of such numerical accuracy.
4) In the third example, that of taking the fifth file from each drawer, the sample could be made random by numbering the files in the drawers and selecting them with a random number generator.
The first, relatively unnoticed, idea of designing a pseudo-random number generator by making use of chaotic first order nonlinear difference equation was proposed by Oishi and Inoue [34] in 1982 where they could construct a uniform random number generator with an arbitary Kolmogorov entropy.
00 at Chepstow formed two legs of its 'Race-O 8' jackpot and when the meeting was abandoned due to waterlogging, rather than making it a six-race bet, the Canadian-licensed firm declared Brads House and Marvellous Dream the winners of those races after using a random number generator.
miniHSM reportedly incorporates a true random number generator and offers secure cryptographic key provisioning and storage, while a built-in real-time clock enables such functionality as time-stamping in addition to the standard functions of encryption, digital signing and authentication.
Each month Ernie, a random number generator, produces a list of several million numbers which are matched to the bonds that have been sold.
Some representative topics discussed in the 32 contributions to the symposium include misleading worm signature generators using deliberate noise injection, dataflow anomaly detection, deterring voluntary trace disclosure in re- encryption mix networks, simulatable security and polynominally bounded concurrent composability, retrofitting legacy code for authorization policy enforcement, automatically generating malicious disks using symbolic execution, cache cookies for browser authentication, secrecy of timing-based active watermarking trace-back techniques, and analysis of the Linux random number generator.
By updating its BSAFE Crypto-C and Crypto-J development tools to take advantage of the Random Number Generator (RNG) features in Intel Corp's 810 chipset, RSA Data Security Inc has followed through on promises it made in January 1999 (CI No 3,578).

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