## Appendix B. Kernel Random Number Generator

The Linux kernel implements a strong random number generator that is theoretically capable of generating true random numbers. The random number generator gathers environmental noise from device drivers into an entropy pool. This pool is accessible from both user processes and the kernel as a source of data that is not only random but also non-deterministic to an outside attacker. Such numbers are of use in various applications, most notably cryptography.

True random numbers differ from the pseudo-random numbers generated by functions such as those found in the C library. Pseudo-random numbers are created by a deterministic function. Although the function may generate a sequence of numbers that exhibit some properties of a true random number, they are only statistically random. Pseudo-random numbers are deterministic: Knowing one number in the sequence provides information about the rest of the sequence. In fact, the initial value of the sequence (known as the seed) usually determines the entire sequence. For applications that need truly random and non-deterministic numbers, such as cryptography, a pseudo-random number is usually unacceptable.

As opposed to a pseudo-random number, a true random is produced independently of its generating function. Further, knowing some value in a sequence of truly random numbers does not enable an external party to deduce future values from the generator because the generator is non-deterministic.

From physics, entropy is a measurement of disorder and randomness in a system. In thermodynamics, entropy is measured in energy per unit temperature (Joules/Kelvin). When Claude Shannon[1], the founder of information theory, looked for a term to represent randomness in information, the great mathematician John von Neumann[2] supposedly suggested he use the term entropy because no one really understand what that meant anyhow. Shannon agreed, and today the term is sometimes called Shannon entropy. In hindsight, some scientists find the dual use confusing, and prefer simply the term uncertainty when discussing information. Kernel hackers, on the other hand, think entropy sounds cool and encourage its use.

[1] Claude E. Shannon (April 30, 1916February 24, 2001) was an engineer at Bell Labs whose most famous work, A Mathematical Theory of Communication, published in 1948, introduced the concept of information theory and Shannon entropy. Shannon also enjoyed riding his unicycle.

[2] John von Neumann (December 28, 1903February 8, 1957) was a member of the Institute for Advanced Study at Princeton. In his life, he made numerous contributions to mathematics, economics, and computer science. Some of his most important contributions were game theory, Neumann algebras, and the von Neumann bottleneck.

In discussions of random number generators, Shannon entropy is an important property. It is measured in bits per symbol; high entropy implies there is little useful information (but lots of random junk) in a sequence of characters. The kernel maintains an entropy pool that is fed data obtained from non-deterministic device events. Ideally, this pool is entirely random. To help keep track of the entropy in the pool, the kernel keeps a measurement of the uncertainty of the data in the pool. As the kernel feeds data into the pool, it estimates the amount of randomness in the data that it is adding. Conversely, the kernel keeps track of entropy as it is removed from the pool. This measurement is called the entropy estimate. Optionally, the kernel can refuse a request for a random number if the entropy estimate is zero.

The kernel random number generator was introduced in kernel version 1.3.30 and lives at drivers/char/random.c in the kernel source.