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.\" $FreeBSD: head/share/man/man4/random.4 301719 2016-06-09 06:55:00Z trasz $
.\"
.Dd August 17, 2015
.Dt RANDOM 4
.Os
.Sh NAME
.Nm random
.Nd the entropy device
.Sh SYNOPSIS
.Cd "device random"
.Cd "options RANDOM_LOADABLE"
.Cd "options RANDOM_ENABLE_UMA"
.Sh DESCRIPTION
The
.Nm
device
returns an endless supply of random bytes when read.
It also accepts and reads data
as any ordinary file.
.Pp
The generator will start in an
.Em unseeded
state, and will block reads until
it is seeded for the first time.
This may cause trouble at system boot
when keys and the like
are generated from
.Nm
so steps should be taken to ensure a
seeding as soon as possible.
.Pp
It is also possible
to read random bytes
by using the KERN_ARND sysctl.
On the command line
this could be done by
.Pp
.Dl "sysctl -x -B 16 kern.arandom"
.Pp
This sysctl will not return
random bytes unless
the
.Nm
device is seeded.
.Pp
This initial seeding
of random number generators
is a bootstrapping problem
that needs very careful attention.
In some cases,
it may be difficult
to find enough randomness
to seed a random number generator
until a system is fully operational,
but the system requires random numbers
to become fully operational.
It is (or more accurately should be)
critically important that the
.Nm
device is seeded
before the first time it is used.
In the case where a dummy or "blocking-only"
device is used,
it is the responsibility
of the system architect
to ensure that no blocking reads
hold up critical processes.
.Pp
To see the current settings of the software
.Nm
device, use the command line:
.Pp
.Dl "sysctl kern.random"
.Pp
which results in something like:
.Bd -literal -offset indent
kern.random.fortuna.minpoolsize: 64
kern.random.harvest.mask_symbolic: [HIGH_PERFORMANCE], ... ,CACHED
kern.random.harvest.mask_bin: 00111111111
kern.random.harvest.mask: 511
kern.random.random_sources: 'Intel Secure Key RNG'
.Ed
.Pp
Other than
.Dl kern.random.fortuna.minpoolsize
and
.Dl kern.random.harvest.mask
all settings are read-only.
.Pp
The
.Pa kern.random.fortuna.minpoolsize
sysctl is used
to set the seed threshold.
A smaller number gives a faster seed,
but a less secure one.
In practice,
values between 64 and 256
are acceptable.
.Pp
The
.Va kern.random.harvest.mask
bitmask is used to select
the possible entropy sources.
A 0 (zero) value means
the corresponding source
is not considered
as an entropy source.
Set the bit to 1 (one)
if you wish to use
that source.
The
.Va kern.random.harvest.mask_bin
and
.Va kern.random.harvest.mask_symbolic
sysctls
can be used to confirm
that the choices are correct.
Note that disabled items
in the latter item
are listed in square brackets.
See
.Xr random_harvest 9
for more on the harvesting of entropy.
.Pp
When
.Cd "options RANDOM_LOADABLE"
is used,
the
.Pa /dev/random
device is not created
until an "algorithm module"
is loaded.
Two of these modules
are built by default,
.Em random_fortuna
and
.Em random_yarrow .
The
.Em random_yarrow
module is deprecated,
and will be removed in
.Fx 12.
Use of the Yarrow algorithm
is not encouraged,
but while still present
in the kernel source,
it can be selected with the
.Cd "options RANDOM_YARROW"
kernel option.
Note that these loadable modules
are slightly less efficient
than their compiled-in equivalents.
This is because some functions
must be locked against
load and unload events,
and also must be indirect calls
to allow for removal.
.Pp
When
.Cd "options RANDOM_ENABLE_UMA"
is used,
the
.Pa /dev/random
device will obtain entropy
from the zone allocator.
This is potentially very high rate,
and if so will be of questionable use.
If this is the case,
use of this option
is not recommended.
Determining this is not trivial,
so experimenting and measurement
using tools such as
.Xr dtrace 1
will be required.
.Sh RANDOMNESS
The use of randomness in the field of computing
is a rather subtle issue because randomness means
different things to different people.
Consider generating a password randomly,
simulating a coin tossing experiment or
choosing a random back-off period when a server does not respond.
Each of these tasks requires random numbers,
but the random numbers in each case have different requirements.
.Pp
Generation of passwords, session keys and the like
requires cryptographic randomness.
A cryptographic random number generator should be designed
so that its output is difficult to guess,
even if a lot of auxiliary information is known
(such as when it was seeded, subsequent or previous output, and so on).
On
.Fx ,
seeding for cryptographic random number generators is provided by the
.Nm
device,
which provides real randomness.
The
.Xr arc4random 3
library call provides a pseudo-random sequence
which is generally reckoned to be suitable for
simple cryptographic use.
The OpenSSL library also provides functions for managing randomness
via functions such as
.Xr RAND_bytes 3
and
.Xr RAND_add 3 .
Note that OpenSSL uses the
.Nm
device for seeding automatically.
.Pp
Randomness for simulation is required in engineering or
scientific software and games.
The first requirement of these applications is
that the random numbers produced conform to some well-known,
usually uniform, distribution.
The sequence of numbers should also appear numerically uncorrelated,
as simulation often assumes independence of its random inputs.
Often it is desirable to reproduce
the results of a simulation exactly,
so that if the generator is seeded in the same way,
it should produce the same results.
A peripheral concern for simulation is
the speed of a random number generator.
.Pp
Another issue in simulation is
the size of the state associated with the random number generator, and
how frequently it repeats itself.
For example,
a program which shuffles a pack of cards should have 52!\& possible outputs,
which requires the random number generator to have 52!\& starting states.
This means the seed should have at least log_2(52!) ~ 226 bits of state
if the program is to stand a chance of outputting all possible sequences,
and the program needs some unbiased way of generating these bits.
Again,
the
.Nm
device could be used for seeding here,
but in practice, smaller seeds are usually considered acceptable.
.Pp
.Fx
provides two families of functions which are considered
suitable for simulation.
The
.Xr random 3
family of functions provides a random integer
between 0 to
.if t 2\u\s731\s10\d\(mi1.
.if n (2**31)\(mi1.
The functions
.Xr srandom 3 ,
.Xr initstate 3
and
.Xr setstate 3
are provided for deterministically setting
the state of the generator and
the function
.Xr srandomdev 3
is provided for setting the state via the
.Nm
device.
The
.Xr drand48 3
family of functions are also provided,
which provide random floating point numbers in various ranges.
.Pp
Randomness that is used for collision avoidance
(for example, in certain network protocols)
has slightly different semantics again.
It is usually expected that the numbers will be uniform,
as this produces the lowest chances of collision.
Here again,
the seeding of the generator is very important,
as it is required that different instances of
the generator produce independent sequences.
However, the guessability or reproducibility of the sequence is unimportant,
unlike the previous cases.
.Pp
.Fx
does also provide the traditional
.Xr rand 3
library call,
for compatibility purposes.
However,
it is known to be poor for simulation and
absolutely unsuitable for cryptographic purposes,
so its use is discouraged.
.Sh FILES
.Bl -tag -width ".Pa /dev/random"
.It Pa /dev/random
.El
.Sh SEE ALSO
.Xr arc4random 3 ,
.Xr drand48 3 ,
.Xr rand 3 ,
.Xr RAND_add 3 ,
.Xr RAND_bytes 3 ,
.Xr random 3 ,
.Xr sysctl 8 ,
.Xr random 9
.Rs
.%A Ferguson
.%A Schneier
.%A Kohno
.%B Cryptography Engineering
.%I Wiley
.%O ISBN 978-0-470-47424-2
.Re
.Sh HISTORY
A
.Nm
device appeared in
.Fx 2.2 .
The current software implementation,
introduced in
.Fx 10.0 ,
is by
.An Mark R V Murray ,
and is an implementation of the
.Em Fortuna
algorithm by Ferguson
.Em et al .
It replaces the previous
.Em Yarrow
implementation,
introduced in
.Fx 5.0 .
The Yarrow algorithm
is no longer supported
by its authors,
and is therefore deprecated.