彥繬etwork Working Group Donald E. Eastlake, 3rd
OBSOLETES RFC 1750 Jeffrey I. Schiller
Steve Crocker
Expires December 2004 June 2004
Randomness Requirements for Security
   
Status of This Document
This dacument is intended to become a Best Current Practice.
Comments should be sent to the authors. Distribution is unlimited.
This document is an InternetDraft and is in full conformance with
all provisions of Section 10 of RFC 2026. InternetDrafts are
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Abstract
Security systems are built on strong cryptographic algorithms that
foil pattern analysis attempts. However, the security of these
systems is dependent on generating secret quantities for passwords,
cryptographic keys, and similar quantities. The use of pseudorandom
processes to generate secret quantities can result in pseudo
security. The sophisticated attacker of these security systems may
find it easier to reproduce the environment that produced the secret
quantities, searching the resulting small set of possibilities, than
to locate the quantities in the whole of the potential number space.
Choosing random quantities to foil a resourceful and motivated
adversary is surprisingly difficult. This document points out many
pitfalls in using traditional pseudorandom number generation
techniques for choosing such quantities. It recommends the use of
truly random hardware techniques and shows that the existing hardware
on many systems can be used for this purpose. It provides suggestions
to ameliorate the problem when a hardware solution is not available.
And it gives examples of how large such quantities need to be for
some applications.
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Acknowledgements
Special thanks to Peter Gutmann, who has permitted the incorporation
of material from his paper "Software Generation of Practically Strong
Random Numbers", and to Paul Hoffman for his extensive comments.
The following other persons (in alphabetic order) have also
contributed substantially to this document:
Tony Hansen, Sandy Harris, Russ Housley
The following persons (in alphabetic order) contributed to RFC 1750,
the predecessor of this document:
David M. Balenson, Don T. Davis, Carl Ellison, Marc Horowitz,
Christian Huitema, Charlie Kaufman, Steve Kent, Hal Murray, Neil
Haller, Richard Pitkin, Tim Redmond, and Doug Tygar.
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Table of Contents
Status of This Document....................................1
Abstract...................................................1
Acknowledgements...........................................2
Table of Contents..........................................3
1. Introduction............................................5
2. General Requirements....................................6
3. Traditional PseudoRandom Sequences.....................8
4. Unpredictability.......................................10
4.1 Problems with Clocks and Serial Numbers...............10
4.2 Timing and Content of External Events.................11
4.3 The Fallacy of Complex Manipulation...................11
4.4 The Fallacy of Selection from a Large Database........12
5. Hardware for Randomness................................13
5.1 Volume Required.......................................13
5.2 Sensitivity to Skew...................................13
5.2.1 Using Stream Parity to DeSkew......................14
5.2.2 Using Transition Mappings to DeSkew................15
5.2.3 Using FFT to DeSkew................................16
5.2.4 Using Compression to DeSkew........................16
5.3 Existing Hardware Can Be Used For Randomness..........17
5.3.1 Using Existing Sound/Video Input....................17
5.3.2 Using Existing Disk Drives..........................17
5.4 Ring Oscillator Sources...............................18
6. Recommended Software Strategy..........................19
6.1 Mixing Functions......................................19
6.1.1 A Trivial Mixing Function...........................19
6.1.2 Stronger Mixing Functions...........................20
6.1.3 DiffieHellman as a Mixing Function.................22
6.1.4 Using a Mixing Function to Stretch Random Bits......22
6.1.5 Other Factors in Choosing a Mixing Function.........23
6.2 NonHardware Sources of Randomness....................23
6.3 Cryptographically Strong Sequences....................24
6.3.1 Traditional Strong Sequences........................25
6.3.2 The Blum Blum Shub Sequence Generator...............26
6.3.3 Entropy Pool Techniques.............................27
7. Key Generation Standards and Examples..................28
7.1 US DoD Recommendations for Password Generation........28
7.2 X9.17 Key Generation..................................28
7.3 DSS PseudoRandom Number Generation...................29
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7.4 X9.82 PseudoRandom Number Generation.................30
7.5 The /dev/random Device................................30
8. Examples of Randomness Required........................32
8.1 Password Generation..................................32
8.2 A Very High Security Cryptographic Key................33
8.2.1 Effort per Key Trial................................33
8.2.2 Meet in the Middle Attacks..........................34
8.2.3 Other Considerations................................35
9. Conclusion.............................................36
10. Security Considerations...............................37
11. Intellectual Property Considerations..................37
12. Copyright and Disclaimer..............................37
13. Appendix A: Changes from RFC 1750.....................38
14. Informative References................................39
Authors Addresses.........................................43
File Name and Expiration..................................43
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1. Introduction
Software cryptography is coming into wider use and is continuing to
spread, although there is a long way to go until it becomes
pervasive.
Systems like SSH, IPSEC, TLS, S/MIME, PGP, DNSSEC, Kerberos, etc. are
maturing and becoming a part of the network landscape [SSH, IPSEC,
MAIL*, TLS, DNSSEC]. By comparison, when the previous version of this
document [RFC 1750] was issued in 1994, about the only Internet
cryptographic security specification in the IETF was the Privacy
Enhanced Mail protocol [MAIL PEM].
These systems provide substantial protection against snooping and
spoofing. However, there is a potential flaw. At the heart of all
cryptographic systems is the generation of secret, unguessable (i.e.,
random) numbers.
The lack of generally available facilities for generating such
unpredictable numbers is an open wound in the design of cryptographic
software. For the software developer who wants to build a key or
password generation procedure that runs on a wide range of hardware,
the only safe strategy so far has been to force the local
installation to supply a suitable routine to generate random numbers.
This is an awkward, errorprone and unpalatable solution.
It is important to keep in mind that the requirement is for data that
an adversary has a very low probability of guessing or determining.
This can easily fail if pseudorandom data is used which only meets
traditional statistical tests for randomness or which is based on
limited range sources, such as clocks. Frequently such random
quantities are determinable by an adversary searching through an
embarrassingly small space of possibilities.
This Best Current Practice describes techniques for producing random
quantities that will be resistant to such attack. It recommends that
future systems include hardware random number generation or provide
access to existing hardware that can be used for this purpose. It
suggests methods for use if such hardware is not available. And it
gives some estimates of the number of random bits required for sample
applications.
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2. General Requirements
A commonly encountered randomness requirement today is the user
password. This is usually a simple character string. Obviously, if a
password can be guessed, it does not provide security. (For reusable
passwords, it is desirable that users be able to remember the
password. This may make it advisable to use pronounceable character
strings or phrases composed on ordinary words. But this only affects
the format of the password information, not the requirement that the
password be very hard to guess.)
Many other requirements come from the cryptographic arena.
Cryptographic techniques can be used to provide a variety of services
including confidentiality and authentication. Such services are based
on quantities, traditionally called "keys", that are unknown to and
unguessable by an adversary.
In some cases, such as the use of symmetric encryption with the one
time pads or the US Data Encryption Standard [DES] or Advanced
Encryption Standard [AES], the parties who wish to communicate
confidentially and/or with authentication must all know the same
secret key. In other cases, using what are called asymmetric or
"public key" cryptographic techniques, keys come in pairs. One key of
the pair is private and must be kept secret by one party, the other
is public and can be published to the world. It is computationally
infeasible to determine the private key from the public key and
knowledge of the public is of no help to an adversary [ASYMMETRIC].
[SCHNEIER, FERGUSON, KAUFMAN]
The frequency and volume of the requirement for random quantities
differs greatly for different cryptographic systems. Using pure RSA,
random quantities are required only when a new key pair is generated;
thereafter any number of messages can be signed without a further
need for randomness. The public key Digital Signature Algorithm
devised by the US National Institute of Standards and Technology
(NIST) requires good random numbers for each signature [DSS]. And
encrypting with a one time pad, in principle the strongest possible
encryption technique, requires a volume of randomness equal to all
the messages to be processed. [SCHNEIER, FERGUSON, KAUFMAN]
In most of these cases, an adversary can try to determine the
"secret" key by trial and error. (This is possible as long as the key
is enough smaller than the message that the correct key can be
uniquely identified.) The probability of an adversary succeeding at
this must be made acceptably low, depending on the particular
application. The size of the space the adversary must search is
related to the amount of key "information" present in the information
theoretic sense [SHANNON]. This depends on the number of different
secret values possible and the probability of each value as follows:
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\
Bitsofinformation = \  p * log ( p )
/ i 2 i
/

where i counts from 1 to the number of possible secret values and p
sub i is the probability of the value numbered i. (Since p sub i is
less than one, the log will be negative so each term in the sum will
be nonnegative.)
If there are 2^n different values of equal probability, then n bits
of information are present and an adversary would, on the average,
have to try half of the values, or 2^(n1) , before guessing the
secret quantity. If the probability of different values is unequal,
then there is less information present and fewer guesses will, on
average, be required by an adversary. In particular, any values that
the adversary can know are impossible, or are of low probability, can
be initially ignored by an adversary, who will search through the
more probable values first.
For example, consider a cryptographic system that uses 128 bit keys.
If these 128 bit keys are derived by using a fixed pseudorandom
number generator that is seeded with an 8 bit seed, then an adversary
needs to search through only 256 keys (by running the pseudorandom
number generator with every possible seed), not the 2^128 keys that
may at first appear to be the case. Only 8 bits of "information" are
in these 128 bit keys.
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3. Traditional PseudoRandom Sequences
Most traditional sources of random numbers use deterministic sources
of "pseudorandom" numbers. These typically start with a "seed"
quantity and use numeric or logical operations to produce a sequence
of values.
[KNUTH] has a classic exposition on pseudorandom numbers.
Applications he mentions are simulation of natural phenomena,
sampling, numerical analysis, testing computer programs, decision
making, and games. None of these have the same characteristics as the
sort of security uses we are talking about. Only in the last two
could there be an adversary trying to find the random quantity.
However, in these cases, the adversary normally has only a single
chance to use a guessed value. In guessing passwords or attempting to
break an encryption scheme, the adversary normally has many, perhaps
unlimited, chances at guessing the correct value. They can store the
message they are trying to break and repeatedly attack it. They are
also be assumed to be aided by a computer.
For testing the "randomness" of numbers, Knuth suggests a variety of
measures including statistical and spectral. These tests check things
like autocorrelation between different parts of a "random" sequence
or distribution of its values. But they could be met by a constant
stored random sequence, such as the "random" sequence printed in the
CRC Standard Mathematical Tables [CRC]. Despite meeting all the tests
suggested by Knuth, that sequence is unsuitable for cryptographic use
as adversaries must be assumed to have copies of all common published
"random" sequences and will able to spot the source and predict
future values.
A typical pseudorandom number generation technique, known as a
linear congruence pseudorandom number generator, is modular
arithmetic where the value numbered N+1 is calculated from the value
numbered N by
V = ( V * a + b )(Mod c)
N+1 N
The above technique has a strong relationship to linear shift
register pseudorandom number generators, which are well understood
cryptographically [SHIFT*]. In such generators bits are introduced at
one end of a shift register as the Exclusive Or (binary sum without
carry) of bits from selected fixed taps into the register. For
example:
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++ ++ ++ ++
 B  <  B  <  B  < . . . . . . <  B  <+
 0   1   2   n  
++ ++ ++ ++ 
   
  V ++
 V +>  
V 们们们眉贸贸贸贸贸贸贸贸贸贸贸贸贸贸贸贸裙 +>  XOR 
+>  
++
V = ( ( V * 2 ) + B .xor. B ... )(Mod 2^n)
N+1 N 0 2
The goodness of traditional pseudorandom number generator algorithms
is measured by statistical tests on such sequences. Carefully chosen
values a, b, c, and initial V or the placement of shift register tap
in the above simple processes can produce excellent statistics.
These sequences may be adequate in simulations (Monte Carlo
experiments) as long as the sequence is orthogonal to the structure
of the space being explored. Even there, subtle patterns may cause
problems. However, such sequences are clearly bad for use in security
applications. They are fully predictable if the initial state is
known. Depending on the form of the pseudorandom number generator,
the sequence may be determinable from observation of a short portion
of the sequence [SCHNEIER, STERN]. For example, with the generators
above, one can determine V(n+1) given knowledge of V(n). In fact, it
has been shown that with these techniques, even if only one bit of
the pseudorandom values are released, the seed can be determined
from short sequences.
Not only have linear congruent generators been broken, but techniques
are now known for breaking all polynomial congruent generators.
[KRAWCZYK]
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4. Unpredictability
Statistically tested randomness in the traditional sense described in
section 3 is NOT the same as the unpredictability required for
security use.
For example, use of a widely available constant sequence, such as
that from the CRC tables, is very weak against an adversary. Once
they learn of or guess it, they can easily break all security, future
and past, based on the sequence. [CRC] Yet the statistical properties
of these tables are good.
The following sections describe the limitations of some randomness
generation techniques and sources.
4.1 Problems with Clocks and Serial Numbers
Computer clocks, or similar operating system or hardware values,
provide significantly fewer real bits of unpredictability than might
appear from their specifications.
Tests have been done on clocks on numerous systems and it was found
that their behavior can vary widely and in unexpected ways. One
version of an operating system running on one set of hardware may
actually provide, say, microsecond resolution in a clock while a
different configuration of the "same" system may always provide the
same lower bits and only count in the upper bits at much lower
resolution. This means that successive reads on the clock may produce
identical values even if enough time has passed that the value
"should" change based on the nominal clock resolution. There are also
cases where frequently reading a clock can produce artificial
sequential values because of extra code that checks for the clock
being unchanged between two reads and increases it by one! Designing
portable application code to generate unpredictable numbers based on
such system clocks is particularly challenging because the system
designer does not always know the properties of the system clocks
that the code will execute on.
Use of hardware serial numbers such as an Ethernet addresses may also
provide fewer bits of uniqueness than one would guess. Such
quantities are usually heavily structured and subfields may have only
a limited range of possible values or values easily guessable based
on approximate date of manufacture or other data. For example, it is
likely that a company that manufactures both computers and Ethernet
adapters will, at least internally, use its own adapters, which
significantly limits the range of builtin addresses.
Problems such as those described above related to clocks and serial
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numbers make code to produce unpredictable quantities difficult if
the code is to be ported across a variety of computer platforms and
systems.
4.2 Timing and Content of External Events
It is possible to measure the timing and content of mouse movement,
key strokes, and similar user events. This is a reasonable source of
unguessable data with some qualifications. On some machines, inputs
such as key strokes are buffered. Even though the user's inter
keystroke timing may have sufficient variation and unpredictability,
there might not be an easy way to access that variation. Another
problem is that no standard method exists to sample timing details.
This makes it hard to build standard software intended for
distribution to a large range of machines based on this technique.
The amount of mouse movement or the keys actually hit are usually
easier to access than timings but may yield less unpredictability as
the user may provide highly repetitive input.
Other external events, such as network packet arrival times, can also
be used, with care. In particular, the possibility of manipulation of
such times by an adversary and the lack of history at system start up
must be considered.
4.3 The Fallacy of Complex Manipulation
One strategy which may give a misleading appearance of
unpredictability is to take a very complex algorithm (or an excellent
traditional pseudorandom number generator with good statistical
properties) and calculate a cryptographic key by starting with
limited data such as the computer system clock value as the seed. An
adversary who knew roughly when the generator was started would have
a relatively small number of seed values to test as they would know
likely values of the system clock. Large numbers of pseudorandom
bits could be generated but the search space an adversary would need
to check could be quite small.
Thus very strong and/or complex manipulation of data will not help if
the adversary can learn what the manipulation is and there is not
enough unpredictability in the starting seed value. They can usually
use the limited number of results stemming from a limited number of
seed values to defeat security.
Another serious strategy error is to assume that a very complex
pseudorandom number generation algorithm will produce strong random
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numbers when there has been no theory behind or analysis of the
algorithm. There is a excellent example of this fallacy right near
the beginning of Chapter 3 in [KNUTH] where the author describes a
complex algorithm. It was intended that the machine language program
corresponding to the algorithm would be so complicated that a person
trying to read the code without comments wouldn't know what the
program was doing. Unfortunately, actual use of this algorithm showed
that it almost immediately converged to a single repeated value in
one case and a small cycle of values in another case.
Not only does complex manipulation not help you if you have a limited
range of seeds but blindly chosen complex manipulation can destroy
the randomness in a good seed!
4.4 The Fallacy of Selection from a Large Database
Another strategy that can give a misleading appearance of
unpredictability is selection of a quantity randomly from a database
and assume that its strength is related to the total number of bits
in the database. For example, typical USENET servers process many
megabytes of information per day [USENET]. Assume a random quantity
was selected by fetching 32 bytes of data from a random starting
point in this data. This does not yield 32*8 = 256 bits worth of
unguessability. Even after allowing that much of the data is human
language and probably has no more than 2 or 3 bits of information per
byte, it doesn't yield 32*2 = 64 bits of unguessability. For an
adversary with access to the same usenet database the unguessability
rests only on the starting point of the selection. That is perhaps a
little over a couple of dozen bits of unguessability.
The same argument applies to selecting sequences from the data on a
publicly available CD/DVD recording or any other large public
database. If the adversary has access to the same database, this
"selection from a large volume of data" step buys little. However,
if a selection can be made from data to which the adversary has no
access, such as system buffers on an active multiuser system, it may
be of help.
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5. Hardware for Randomness
Is there any hope for true strong portable randomness in the future?
There might be. All that's needed is a physical source of
unpredictable numbers.
A thermal noise (sometimes called Johnson noise in integrated
circuits) or radioactive decay source and a fast, freerunning
oscillator would do the trick directly [GIFFORD]. This is a trivial
amount of hardware, and could easily be included as a standard part
of a computer system's architecture. Furthermore, any system with a
spinning disk or ring oscillator and a stable (crystal) time source
or the like has an adequate source of randomness ([DAVIS] and Section
5.4). All that's needed is the common perception among computer
vendors that this small additional hardware and the software to
access it is necessary and useful.
5.1 Volume Required
How much unpredictability is needed? Is it possible to quantify the
requirement in, say, number of random bits per second?
The answer is not very much is needed. For AES, the key can be 128
bits and, as we show in an example in Section 8, even the highest
security system is unlikely to require strong keying material of much
over 200 bits. If a series of keys are needed, they can be generated
from a strong random seed (starting value) using a cryptographically
strong sequence as explained in Section 6.3. A few hundred random
bits generated at start up or once a day would be enough using such
techniques. Even if the random bits are generated as slowly as one
per second and it is not possible to overlap the generation process,
it should be tolerable in most high security applications to wait 200
seconds occasionally.
These numbers are trivial to achieve. It could be done by a person
repeatedly tossing a coin. Almost any hardware based process is
likely to be much faster.
5.2 Sensitivity to Skew
Is there any specific requirement on the shape of the distribution of
the random numbers? The good news is the distribution need not be
uniform. All that is needed is a conservative estimate of how non
uniform it is to bound performance. Simple techniques to deskew the
bit stream are given below and stronger cryptographic techniques are
described in Section 6.1.2 below.
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5.2.1 Using Stream Parity to DeSkew
Consider taking a sufficiently long string of bits and map the string
to "zero" or "one". The mapping will not yield a perfectly uniform
distribution, but it can be as close as desired. One mapping that
serves the purpose is to take the parity of the string. This has the
advantages that it is robust across all degrees of skew up to the
estimated maximum skew and is absolutely trivial to implement in
hardware.
The following analysis gives the number of bits that must be sampled:
Suppose the ratio of ones to zeros is ( 0.5 + e ) to ( 0.5  e ),
where e is between 0 and 0.5 and is a measure of the "eccentricity"
of the distribution. Consider the distribution of the parity function
of N bit samples. The probabilities that the parity will be one or
zero will be the sum of the odd or even terms in the binomial
expansion of (p + q)^N, where p = 0.5 + e, the probability of a one,
and q = 0.5  e, the probability of a zero.
These sums can be computed easily as
N N
1/2 * ( ( p + q ) + ( p  q ) )
and
N N
1/2 * ( ( p + q )  ( p  q ) ).
(Which one corresponds to the probability the parity will be 1
depends on whether N is odd or even.)
Since p + q = 1 and p  q = 2e, these expressions reduce to
N
1/2 * [1 + (2e) ]
and
N
1/2 * [1  (2e) ].
Neither of these will ever be exactly 0.5 unless e is zero, but we
can bring them arbitrarily close to 0.5. If we want the probabilities
to be within some delta d of 0.5, i.e. then
N
( 0.5 + ( 0.5 * (2e) ) ) < 0.5 + d.
Solving for N yields N > log(2d)/log(2e). (Note that 2e is less than
1, so its log is negative. Division by a negative number reverses the
sense of an inequality.)
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The following table gives the length of the string which must be
sampled for various degrees of skew in order to come within 0.001 of
a 50/50 distribution.
++++
 Prob(1)  e  N 
++++
 0.5  0.00  1 
 0.6  0.10  4 
 0.7  0.20  7 
 0.8  0.30  13 
 0.9  0.40  28 
 0.95  0.45  59 
 0.99  0.49  308 
++++
The last entry shows that even if the distribution is skewed 99% in
favor of ones, the parity of a string of 308 samples will be within
0.001 of a 50/50 distribution.
5.2.2 Using Transition Mappings to DeSkew
Another technique, originally due to von Neumann [VON NEUMANN], is to
examine a bit stream as a sequence of nonoverlapping pairs. You
could then discard any 00 or 11 pairs found, interpret 01 as a 0 and
10 as a 1. Assume the probability of a 1 is 0.5+e and the probability
of a 0 is 0.5e where e is the eccentricity of the source and
described in the previous section. Then the probability of each pair
is as follows:
+++
 pair  probability 
+++
 00  (0.5  e)^2 = 0.25  e + e^2 
 01  (0.5  e)*(0.5 + e) = 0.25  e^2 
 10  (0.5 + e)*(0.5  e) = 0.25  e^2 
 11  (0.5 + e)^2 = 0.25 + e + e^2 
+++
This technique will completely eliminate any bias but at the expense
of taking an indeterminate number of input bits for any particular
desired number of output bits. The probability of any particular pair
being discarded is 0.5 + 2e^2 so the expected number of input bits to
produce X output bits is X/(0.25  e^2).
This technique assumes that the bits are from a stream where each bit
has the same probability of being a 0 or 1 as any other bit in the
stream and that bits are not correlated, i.e., that the bits are
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identical independent distributions. If alternate bits were from two
correlated sources, for example, the above analysis breaks down.
The above technique also provides another illustration of how a
simple statistical analysis can mislead if one is not always on the
lookout for patterns that could be exploited by an adversary. If the
algorithm were misread slightly so that overlapping successive bits
pairs were used instead of nonoverlapping pairs, the statistical
analysis given is the same; however, instead of providing an unbiased
uncorrelated series of random 1's and 0's, it instead produces a
totally predictable sequence of exactly alternating 1's and 0's.
5.2.3 Using FFT to DeSkew
When real world data consists of strongly biased or correlated bits,
it may still contain useful amounts of randomness. This randomness
can be extracted through use of the discrete Fourier transform or its
optimized variant, the FFT.
Using the Fourier transform of the data, strong correlations can be
discarded. If adequate data is processed and remaining correlations
decay, spectral lines approaching statistical independence and
normally distributed randomness can be produced [BRILLINGER].
5.2.4 Using Compression to DeSkew
Reversible compression techniques also provide a crude method of de
skewing a skewed bit stream. This follows directly from the
definition of reversible compression and the formula in Section 2
above for the amount of information in a sequence. Since the
compression is reversible, the same amount of information must be
present in the shorter output than was present in the longer input.
By the Shannon information equation, this is only possible if, on
average, the probabilities of the different shorter sequences are
more uniformly distributed than were the probabilities of the longer
sequences. Therefore the shorter sequences must be deskewed relative
to the input.
However, many compression techniques add a somewhat predictable
preface to their output stream and may insert such a sequence again
periodically in their output or otherwise introduce subtle patterns
of their own. They should be considered only a rough technique
compared with those described above or in Section 6.1.2. At a
minimum, the beginning of the compressed sequence should be skipped
and only later bits used for applications requiring random bits.
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5.3 Existing Hardware Can Be Used For Randomness
As described below, many computers come with hardware that can, with
care, be used to generate truly random quantities.
5.3.1 Using Existing Sound/Video Input
Many computers are built with inputs that digitize some real world
analog source, such as sound from a microphone or video input from a
camera. Under appropriate circumstances, such input can provide
reasonably high quality random bits. The "input" from a sound
digitizer with no source plugged in or a camera with the lens cap on,
if the system has enough gain to detect anything, is essentially
thermal noise.
For example, on some UNIX based systems, one can read from the
/dev/audio device with nothing plugged into the microphone jack or
the microphone receiving only low level background noise. Such data
is essentially random noise although it should not be trusted without
some checking in case of hardware failure. It will, in any case, need
to be deskewed as described elsewhere.
Combining this with compression to deskew one can, in UNIXese,
generate a huge amount of medium quality random data by doing
cat /dev/audio  compress  >randombitsfile
5.3.2 Using Existing Disk Drives
Disk drives have small random fluctuations in their rotational speed
due to chaotic air turbulence [DAVIS]. By adding low level disk seek
time instrumentation to a system, a series of measurements can be
obtained that include this randomness. Such data is usually highly
correlated so that significant processing is needed, such as FFT (see
section 5.2.3). Nevertheless experimentation has shown that, with
such processing, most disk drives easily produce 100 bits a minute or
more of excellent random data.
Partly offsetting this need for processing is the fact that disk
drive failure will normally be rapidly noticed. Thus, problems with
this method of random number generation due to hardware failure are
unlikely.
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5.4 Ring Oscillator Sources
If an integrated circuit is being designed or field programmed, an
odd number of gates can be connected in series to produce a free
running ring oscillator. By sampling a point in the ring at a fixed
frequency, say one determined by a stable crystal oscillator, some
amount of entropy can be extracted due to variations in the free
running oscillator timing. It is possible to increase the rate of
entropy by xor'ing sampled values from a few ring oscillators with
relatively prime lengths. It is sometimes recommended that an odd
number of rings be used so that, even if the rings somehow become
synchronously locked to each other, there will still be sampled bit
transitions. Another possibility source to sample is the output of a
noisy diode.
Sampled bits from such sources will have to be heavily deskewed, as
disk rotation timings must be (Section 5.3.2). An engineering study
would be needed to determine the amount of entropy being produced
depending on the particular design. In any case, these can be good
sources whose cost is a trivial amount of hardware by modern
standards.
As an example, IEEE 802.11i suggests that the circuit below be
considered, with due attention in the design to isolation of the
rings from each other and from clocked circuits to avoid undesired
synchronization, etc., and extensive post processing. [IEEE 802.11i]
\ \ \
+> >0> >0 19 total  >0+,命葮p4(们们们们们们眉贸贸葼葪们葼+
 / / /  
  
++ V
++
\ \ \   output
+> >0> >0 23 total  >0+> XOR >
裙惹贸贸葼葪们裙惹贸贸们汝阮命孺没孺绵脳们贸么葪们裙惹贸贸眉贸贸贸葼葪命a=H命葪贸贸贸贸贸贸裙  / / /   
  ++
++ ^ ^
 
\ \ \  
+> >0> >0 29 total  >0++ 
 / / 们汝锐命孺没孺绵脳们贸么葪们裙 /  
  
++ 

other randomness if available+
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6. Recommended Software Strategy
What is the best overall strategy for meeting the requirement for
unguessable random numbers in the absence of a reliable hardware
source? It is to obtain random input from a number of uncorrelated
sources and to mix them with a strong mixing function. Such a
function will preserve the randomness present in any of the sources
even if other quantities being combined happen to be fixed or easily
guessable. This may be advisable even with a good hardware source, as
hardware can also fail, though this should be weighed against any
increase in the chance of overall failure due to added software
complexity.
6.1 Mixing Functions
A strong mixing function is one which combines two or more inputs and
produces an output where each output bit is a different complex non
linear function of all the input bits. On average, changing any input
bit will change about half the output bits. But because the
relationship is complex and nonlinear, no particular output bit is
guaranteed to change when any particular input bit is changed.
Consider the problem of converting a stream of bits that is skewed
towards 0 or 1 or which has a somewhat predictable pattern to a
shorter stream which is more random, as discussed in Section 5.2
above. This is simply another case where a strong mixing function is
desired, mixing the input bits to produce a smaller number of output
bits. The technique given in Section 5.2.1 of using the parity of a
number of bits is simply the result of successively Exclusive Or'ing
them which is examined as a trivial mixing function immediately
below. Use of stronger mixing functions to extract more of the
randomness in a stream of skewed bits is examined in Section 6.1.2.
6.1.1 A Trivial Mixing Function
A trivial example for single bit inputs is the Exclusive Or function,
which is equivalent to addition without carry, as show in the table
below. This is a degenerate case in which the one output bit always
changes for a change in either input bit. But, despite its
simplicity, it provides a useful illustration.
D. Eastlake, J. Schiller, S. Crocker [Page 19]
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++++
 input 1  input 2  output 
++++
 0  0  0 
 0  1  1 
 1  0  1 
 1  1  0 
++++
If inputs 1 and 2 are uncorrelated and combined in this fashion then
the output will be an even better (less skewed) random bit than the
inputs. If we assume an "eccentricity" e as defined in Section 5.2
above, then the output eccentricity relates to the input eccentricity
as follows:
e = 2 * e * e
output input 1 input 2
Since e is never greater than 1/2, the eccentricity is always
improved except in the case where at least one input is a totally
skewed constant. This is illustrated in the following table where the
top and left side values are the two input eccentricities and the
entries are the output eccentricity:
++++++++
 e  0.00  0.10  0.20  0.30  0.40  0.50 
++++++++
 0.00  0.00  0.00  0.00  0.00  0.00  0.00 
 0.10  0.00  0.02  0.04  0.06  0.08  0.10 
 0.20  0.00  0.04  0.08  0.12  0.16  0.20 
 0.30  0.00  0.06  0.12  0.18  0.24  0.30 
 0.40  0.00  0.08  0.16  0.24  0.32  0.40 
 0.50  0.00  0.10  0.20  0.30  0.40  0.50 
++++++++
However, keep in mind that the above calculations assume that the
inputs are not correlated. If the inputs were, say, the parity of the
number of minutes from midnight on two clocks accurate to a few
seconds, then each might appear random if sampled at random intervals
much longer than a minute. Yet if they were both sampled and combined
with xor, the result would be zero most of the time.
6.1.2 Stronger Mixing Functions
The US Government Advanced Encryption Standard [AES] is an example of
a strong mixing function for multiple bit quantities. It takes up to
384 bits of input (128 bits of "data" and 256 bits of "key") and
produces 128 bits of output each of which is dependent on a complex
D. Eastlake, J. Schiller, S. Crocker [Page 20]
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nonlinear function of all input bits. Other encryption functions
with this characteristic, such as [DES], can also be used by
considering them to mix all of their key and data input bits.
Another good family of mixing functions are the "message digest" or
hashing functions such as The US Government Secure Hash Standards
[SHA*] and the MD4, MD5 [MD4, MD5] series. These functions all take a
practically unlimited amount of input and produce a relatively short
fixed length output mixing all the input bits. The MD* series produce
128 bits of output, SHA1 produces 160 bits, and other SHA functions
produce up to 512 bits.
Although the message digest functions are designed for variable
amounts of input, AES and other encryption functions can also be used
to combine any number of inputs. If 128 bits of output is adequate,
the inputs can be packed into a 128bit data quantity and successive
AES keys, padding with zeros if needed, which are then used to
successively encrypt using AES in Electronic Codebook Mode. Or the
input could be packed into one 128bit key and multiple data blocks
and a CBCMAC calculated [MODES].
If more than 128 bits of output are needed, use more complex mixing.
But keep in mind that it is absolutely impossible to get more bits of
"randomness" out than are put in. For example, if inputs are packed
into three quantities, A, B, and C, use AES to encrypt A with B as a
key and then with C as a key to produce the 1st part of the output,
then encrypt B with C and then A for more output and, if necessary,
encrypt C with A and then B for yet more output. Still more output
can be produced by reversing the order of the keys given above to
stretch things. The same can be done with the hash functions by
hashing various subsets of the input data or different copies of the
input data with different prefixes and/or suffixes to produce
multiple outputs.
Many modern block encryption functions, including DES and AES,
incorporate modules known as SBoxes (substitution boxes). These
produce a smaller number of outputs from a larger number of inputs
through a complex nonlinear mixing function which would have the
effect of concentrating limited entropy in the inputs into the
output.
SBoxes sometimes incorporate bent boolean functions (functions of an
even number of bits producing one output bit with maximum non
linearity). Looking at the output for all input pairs differing in
any particular bit position, exactly half the outputs are different.
An SBox in which each output bit is produced by a bent function such
that any linear combination of these functions is also a bent
function is called a "perfect SBox".
Sboxes and various repeated application or cascades of such boxes
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can be used for mixing. [SBOX*]
An example of using a strong mixing function would be to reconsider
the case of a string of 308 bits each of which is biased 99% towards
zero. The parity technique given in Section 5.2.1 above reduced this
to one bit with only a 1/1000 deviance from being equally likely a
zero or one. But, applying the equation for information given in
Section 2, this 308 bit skewed sequence has over 5 bits of
information in it. Thus hashing it with SHA1 and taking the bottom 5
bits of the result would yield 5 unbiased random bits as opposed to
the single bit given by calculating the parity of the string.
6.1.3 DiffieHellman as a Mixing Function
DiffieHellman exponential key exchange is a technique that yields a
shared secret between two parties that can be made computationally
infeasible for a third party to determine even if they can observe
all the messages between the two communicating parties. This shared
secret is a mixture of initial quantities generated by each of them
[DH]. If these initial quantities are random, then the shared secret
contains the combined randomness of them both, assuming they are
uncorrelated.
6.1.4 Using a Mixing Function to Stretch Random Bits
While it is not necessary for a mixing function to produce the same
or fewer bits than its inputs, mixing bits cannot "stretch" the
amount of random unpredictability present in the inputs. Thus four
inputs of 32 bits each where there is 12 bits worth of
unpredictability (such as 4,096 equally probable values) in each
input cannot produce more than 48 bits worth of unpredictable output.
The output can be expanded to hundreds or thousands of bits by, for
example, mixing with successive integers, but the clever adversary's
search space is still 2^48 possibilities. Furthermore, mixing to
fewer bits than are input will tend to strengthen the randomness of
the output the way using Exclusive Or to produce one bit from two did
above.
The last table in Section 6.1.1 shows that mixing a random bit with a
constant bit with Exclusive Or will produce a random bit. While this
is true, it does not provide a way to "stretch" one random bit into
more than one. If, for example, a random bit is mixed with a 0 and
then with a 1, this produces a two bit sequence but it will always be
either 01 or 10. Since there are only two possible values, there is
still only the one bit of original randomness.
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6.1.5 Other Factors in Choosing a Mixing Function
For local use, AES has the advantages that it has been widely tested
for flaws, is reasonably efficient in software, and is widely
documented and implemented with hardware and software implementations
available all over the world including open source code. The SHA*
family have had a little less study and tend to require more CPU
cycles than AES but there is no reason to believe they are flawed.
Both SHA* and MD5 were derived from the earlier MD4 algorithm. They
all have source code available [SHA*, MD*]. Some signs of weakness
have been found in MD4 and MD5. In particular, MD4 has only three
rounds and there are several independent breaks of the first two or
last two rounds. And some collisions have been found in MD5 output.
AES was selected by a robust, public, and international process. It
and SHA* have been vouched for by the US National Security Agency
(NSA) on the basis of criteria that mostly remain secret, as was DES.
While this has been the cause of much speculation and doubt,
investigation of DES over the years has indicated that NSA
involvement in modifications to its design, which originated with
IBM, was primarily to strengthen it. No concealed or special weakness
has been found in DES. It is likely that the NSA modifications to MD4
to produce the SHA algorithms similarly strengthened these
algorithms, possibly against threats not yet known in the public
cryptographic community.
Where input lengths are unpredictable, hash algorithms are a little
more convenient to use than block encryption algorithms since they
are generally designed to accept variable length inputs. Block
encryption algorithms generally require an additional padding
algorithm to accomodate inputs that are not an even multiple of the
block size.
As of the time of this document, the authors know of no patent claims
to the basic AES, DES, SHA*, MD4, and MD5 algorithms other than
patents for which an irrevocable royalty free license has been
granted to the world. There may, of course, be basic patents of which
the authors are unaware or patents on implementations or uses or
other relevant patents issued or to be issued.
6.2 NonHardware Sources of Randomness
The best source of input for mixing would be a hardware randomness
such as ring oscillators, disk drive timing, thermal noise, or
radioactive decay. However, if that is not available there are other
possibilities. These include system clocks, system or input/output
buffers, user/system/hardware/network serial numbers and/or addresses
and timing, and user input. Unfortunately, each of these sources can
D. Eastlake, J. Schiller, S. Crocker [Page 23]
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produce very limited or predictable values under some circumstances.
Some of the sources listed above would be quite strong on multiuser
systems where, in essence, each user of the system is a source of
randomness. However, on a small single user or embedded system,
especially at start up, it might be possible for an adversary to
assemble a similar configuration. This could give the adversary
inputs to the mixing process that were sufficiently correlated to
those used originally as to make exhaustive search practical.
The use of multiple random inputs with a strong mixing function is
recommended and can overcome weakness in any particular input. The
timing and content of requested "random" user keystrokes can yield
hundreds of random bits but conservative assumptions need to be made.
For example, assuming at most a few bits of randomness if the inter
keystroke interval is unique in the sequence up to that point and a
similar assumption if the key hit is unique but assuming that no bits
of randomness are present in the initial key value or if the timing
or key value duplicate previous values. The results of mixing these
timings and characters typed could be further combined with clock
values and other inputs.
This strategy may make practical portable code to produce good random
numbers for security even if some of the inputs are very weak on some
of the target systems. However, it may still fail against a high
grade attack on small, single user or embedded systems, especially if
the adversary has ever been able to observe the generation process in
the past. A hardware based random source is still preferable.
6.3 Cryptographically Strong Sequences
In cases where a series of random quantities must be generated, an
adversary may learn some values in the sequence. In general, they
should not be able to predict other values from the ones that they
know.
The correct technique is to start with a strong random seed, take
cryptographically strong steps from that seed [FERGUSON, SCHNEIER],
and do not reveal the complete state of the generator in the sequence
elements. If each value in the sequence can be calculated in a fixed
way from the previous value, then when any value is compromised, all
future values can be determined. This would be the case, for example,
if each value were a constant function of the previously used values,
even if the function were a very strong, noninvertible message
digest function.
(It should be noted that if your technique for generating a sequence
of key values is fast enough, it can trivially be used as the basis
D. Eastlake, J. Schiller, S. Crocker [Page 24]
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for a confidentiality system. If two parties use the same sequence
generating technique and start with the same seed material, they will
generate identical sequences. These could, for example, be xor'ed at
one end with data being send, encrypting it, and xor'ed with this
data as received, decrypting it due to the reversible properties of
the xor operation. This is commonly referred to as a simple stream
cipher.)
6.3.1 Traditional Strong Sequences
A traditional way to achieve a strong sequence has been to have the
values be produced by hashing the quantities produced by
concatenating the seed with successive integers or the like and then
mask the values obtained so as to limit the amount of generator state
available to the adversary.
It may also be possible to use an "encryption" algorithm with a
random key and seed value to encrypt and feedback some or all of the
output encrypted value into the value to be encrypted for the next
iteration. Appropriate feedback techniques will usually be
recommended with the encryption algorithm. An example is shown below
where shifting and masking are used to combine the cypher output
feedback. This type of feedback is defined by the US Government in
connection with AES and DES [MODES] as Output Feedback Mode (OFM) but
should be avoided for reasons described below.
++
 V 
  n +
+++ 
  ++
shift +>   ++
++  Encrypt  <  Key 
 +   ++
  ++
V V
+++
 V  
 n+1 
++
Note that if a shift of one is used, this is the same as the shift
register technique described in Section 3 above but with the all
important difference that the feedback is determined by a complex
nonlinear function of all bits rather than a simple linear or
polynomial combination of output from a few bit position taps.
It has been shown by Donald W. Davies that this sort of shifted
D. Eastlake, J. Schiller, S. Crocker [Page 25]
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partial output feedback significantly weakens an algorithm compared
with feeding all of the output bits back as input. In particular, for
DES, repeated encrypting a full 64 bit quantity will give an expected
repeat in about 2^63 iterations. Feeding back anything less than 64
(and more than 0) bits will give an expected repeat in between 2^31
and 2^32 iterations!
To predict values of a sequence from others when the sequence was
generated by these techniques is equivalent to breaking the
cryptosystem or inverting the "noninvertible" hashing involved with
only partial information available. The less information revealed
each iteration, the harder it will be for an adversary to predict the
sequence. Thus it is best to use only one bit from each value. It has
been shown that in some cases this makes it impossible to break a
system even when the cryptographic system is invertible and can be
broken if all of each generated value was revealed.
6.3.2 The Blum Blum Shub Sequence Generator
Currently the generator which has the strongest public proof of
strength is called the Blum Blum Shub generator after its inventors
[BBS]. It is also very simple and is based on quadratic residues.
It's only disadvantage is that it is computationally intensive
compared with the traditional techniques give in 6.3.1 above. This is
not a major draw back if it is used for moderately infrequent
purposes, such as generating session keys.
Simply choose two large prime numbers, say p and q, which both have
the property that you get a remainder of 3 if you divide them by 4.
Let n = p * q. Then you choose a random number x relatively prime to
n. The initial seed for the generator and the method for calculating
subsequent values are then
2
s = ( x )(Mod n)
0
2
s = ( s )(Mod n)
i+1 i
You must be careful to use only a few bits from the bottom of each s.
It is always safe to use only the lowest order bit. If you use no
more than the
log ( log ( s ) )
2 2 i
low order bits, then predicting any additional bits from a sequence
generated in this manner is provable as hard as factoring n. As long
D. Eastlake, J. Schiller, S. Crocker [Page 26]
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as the initial x is secret, you can even make n public if you want.
An interesting characteristic of this generator is that you can
directly calculate any of the s values. In particular
i
( ( 2 )(Mod (( p  1 ) * ( q  1 )) ) )
s = ( s )(Mod n)
i 0
This means that in applications where many keys are generated in this
fashion, it is not necessary to save them all. Each key can be
effectively indexed and recovered from that small index and the
initial s and n.
6.3.3 Entropy Pool Techniques
Many modern pseudorandom number sources utilize the technique of
maintaining a "pool" of bits and providing operations for strongly
mixing input with some randomness into the pool and extracting psuedo
random bits from the pool. This is illustrated in the figure below.
++ ++ ++
> Mix In > POOL > Extract >
们们贸贸贸裙  Bits     Bits 
++ ++ ++
^ V
 
++
Bits to be feed into the pool can be any of the various hardware,
environmental, or user input sources discussed above. It is also
common to save the state of the pool on system shut down and restore
it on restarting, if stable storage is available.
Care must be taken that enough entropy has been added to the pool to
support particular output uses desired. See Section 7.5 for more
details on an example implementation and [RSA BULL1] for similar
suggestions.
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7. Key Generation Standards and Examples
Several public standards and widely deployed examples are now in
place for the generation of keys without special hardware. Three
standards are described below. The two older standards use DES, with
its 64bit block and key size limit, but any equally strong or
stronger mixing function could be substituted. The third is a more
modern and stronger standard based on SHA1. Finally the widely
deployed modern UNIX random number generators are described.
7.1 US DoD Recommendations for Password Generation
The United States Department of Defense has specific recommendations
for password generation [DoD]. They suggest using the US Data
Encryption Standard [DES] in Output Feedback Mode [MODES] as follows:
use an initialization vector determined from
the system clock,
system ID,
user ID, and
date and time;
use a key determined from
system interrupt registers,
system status registers, and
system counters; and,
as plain text, use an external randomly generated 64 bit
quantity such as 8 characters typed in by a system
administrator.
The password can then be calculated from the 64 bit "cipher text"
generated by DES in 64bit Output Feedback Mode. As many bits as are
needed can be taken from these 64 bits and expanded into a
pronounceable word, phrase, or other format if a human being needs to
remember the password.
7.2 X9.17 Key Generation
The American National Standards Institute has specified a method for
generating a sequence of keys as follows [X9.17]:
s is the initial 64 bit seed
0
g is the sequence of generated 64 bit key quantities
n
D. Eastlake, J. Schiller, S. Crocker [Page 28]
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k is a random key reserved for generating this key sequence
t is the time at which a key is generated to as fine a resolution
as is available (up to 64 bits).
DES ( K, Q ) is the DES encryption of quantity Q with key K
g = DES ( k, DES ( k, t ) .xor. s )
n n
s = DES ( k, DES ( k, t ) .xor. g )
n+1 n
If g sub n is to be used as a DES key, then every eighth bit should
be adjusted for parity for that use but the entire 64 bit unmodified
g should be used in calculating the next s.
7.3 DSS PseudoRandom Number Generation
Appendix 3 of the NIST Digital Signature Standard [DSS] provides an
approved method of producing a sequence of pseudorandom 160 bit
quantities for use as private keys or the like. A subset of that
algorithm is as follows:
t = 0x 67452301 EFCDAB89 98BADCFE 10325476 C3D2E1F0
q = a 160bit prime number
XKEY = initial seed
0
For j = 0 to ...
XVAL = ( XKEY + optional user input ) (Mod 2^512)
j
X = G( t, XVAL ) (Mod q)
j
XKEY = ( 1 + XKEY + X ) (Mod 2^512)
j+1 j j
The quantities X thus produced are the pseudorandom sequence of
values in the rang 0 to q. Two functions can be used for "G" above.
Each produces a 160bit value and takes two arguments, the first a
160bit value and the second a 512 bit value.
D. Eastlake, J. Schiller, S. Crocker [Page 29]
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The first is based on SHA1 and works by setting the 5 linking
variables, denoted H with subscripts in the SHA1 specification, to
the first argument divided into fifths. Then steps (a) through (e) of
section 7 of the NIST SHA1 specification are run over the second
argument as if it were a 512bit data block. The values of the
linking variable after those steps are then concatenated to produce
the output of G. [SHA1]
As an alternative second methold, NIST also defined an alternate G
function based on multiple applications of the DES encryption
function [DSS].
7.4 X9.82 PseudoRandom Number Generation
The National Institute for Standards and Technology (NIST) and the
American National Standards Institutes (ANSI) X9F1 committee are in
the final stages of creating a standard for random number generation.
This standard includes a number of random number generators for use
with AES and other block ciphers. It also includes random number
generators based on hash functions and the arithmetic of elliptic
curves [X9.82].
7.5 The /dev/random Device
Several versions of the UNIX operating system provides a kernel
resident random number generator. In some cases, these generators
makes use of events captured by the Kernel during normal system
operation.
For example, on some versions of Linux, the generator consists of a
random pool of 512 bytes represented as 128 words of 4bytes each.
When an event occurs, such as a disk drive interrupt, the time of the
event is xor'ed into the pool and the pool is stirred via a primitive
polynomial of degree 128. The pool itself is treated as a ring
buffer, with new data being XORed (after stirring with the
polynomial) across the entire pool.
Each call that adds entropy to the pool estimates the amount of
likely true entropy the input contains. The pool itself contains a
accumulator that estimates the total over all entropy of the pool.
Input events come from several sources as listed below.
Unfortunately, for server machines without human operators, the first
and third are not available and entropy may be added very slowly in
that case.
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1. Keyboard interrupts. The time of the interrupt as well as the scan
code are added to the pool. This in effect adds entropy from the
human operator by measuring interkeystroke arrival times.
2. Disk completion and other interrupts. A system being used by a
person will likely have a hard to predict pattern of disk
accesses. (But not all disk drivers support capturing this timing
information with sufficient accuracy to be useful.)
3. Mouse motion. The timing as well as mouse position is added in.
When random bytes are required, the pool is hashed with SHA1 [SHA1]
to yield the returned bytes of randomness. If more bytes are required
than the output of SHA1 (20 bytes), then the hashed output is
stirred back into the pool and a new hash performed to obtain the
next 20 bytes. As bytes are removed from the pool, the estimate of
entropy is similarly decremented.
To ensure a reasonable random pool upon system startup, the standard
startup scripts (and shutdown scripts) save the pool to a disk file
at shutdown and read this file at system startup.
There are two user exported interfaces. /dev/random returns bytes
from the pool, but blocks when the estimated entropy drops to zero.
As entropy is added to the pool from events, more data becomes
available via /dev/random. Random data obtained from such a
/dev/random device is suitable for key generation for long term keys,
if enough random bits are in the pool or are added in a reasonable
amount of time.
/dev/urandom works like /dev/random, however it provides data even
when the entropy estimate for the random pool drops to zero. This may
be adequate for session keys or for other key generation tasks where
blocking while waiting for more random bits is not acceptable. The
risk of continuing to take data even when the pool's entropy estimate
is small in that past output may be computable from current output
provided an attacker can reverse SHA1. Given that SHA1 is designed
to be noninvertible, this is a reasonable risk.
To obtain random numbers under Linux, Solaris, or other UNIX systems
equiped with code as described above, all an application needs to do
is open either /dev/random or /dev/urandom and read the desired
number of bytes.
(The Linux Random device was written by Theodore Ts'o. It was based
loosely on the random number generator in PGP 2.X and PGP 3.0 (aka
PGP 5.0).)
D. Eastlake, J. Schiller, S. Crocker [Page 31]
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8. Examples of Randomness Required
Below are two examples showing rough calculations of needed
randomness for security. The first is for moderate security passwords
while the second assumes a need for a very high security
cryptographic key.
In addition [ORMAN] and [RSA BULL13] provide information on the
public key lengths that should be used for exchanging symmetric keys.
8.1 Password Generation
Assume that user passwords change once a year and it is desired that
the probability that an adversary could guess the password for a
particular account be less than one in a thousand. Further assume
that sending a password to the system is the only way to try a
password. Then the crucial question is how often an adversary can try
possibilities. Assume that delays have been introduced into a system
so that, at most, an adversary can make one password try every six
seconds. That's 600 per hour or about 15,000 per day or about
5,000,000 tries in a year. Assuming any sort of monitoring, it is
unlikely someone could actually try continuously for a year. In fact,
even if log files are only checked monthly, 500,000 tries is more
plausible before the attack is noticed and steps taken to change
passwords and make it harder to try more passwords.
To have a one in a thousand chance of guessing the password in
500,000 tries implies a universe of at least 500,000,000 passwords or
about 2^29. Thus 29 bits of randomness are needed. This can probably
be achieved using the US DoD recommended inputs for password
generation as it has 8 inputs which probably average over 5 bits of
randomness each (see section 7.1). Using a list of 1000 words, the
password could be expressed as a three word phrase (1,000,000,000
possibilities) or, using case insensitive letters and digits, six
would suffice ((26+10)^6 = 2,176,782,336 possibilities).
For a higher security password, the number of bits required goes up.
To decrease the probability by 1,000 requires increasing the universe
of passwords by the same factor which adds about 10 bits. Thus to
have only a one in a million chance of a password being guessed under
the above scenario would require 39 bits of randomness and a password
that was a four word phrase from a 1000 word list or eight
letters/digits. To go to a one in 10^9 chance, 49 bits of randomness
are needed implying a five word phrase or ten letter/digit password.
In a real system, of course, there are also other factors. For
example, the larger and harder to remember passwords are, the more
likely users are to write them down resulting in an additional risk
D. Eastlake, J. Schiller, S. Crocker [Page 32]
INTERNET DRAFT Randomness Requirements for Security June 2004
of compromise.
8.2 A Very High Security Cryptographic Key
Assume that a very high security key is needed for symmetric
encryption / decryption between two parties. Assume an adversary can
observe communications and knows the algorithm being used. Within the
field of random possibilities, the adversary can try key values in
hopes of finding the one in use. Assume further that brute force
trial of keys is the best the adversary can do.
8.2.1 Effort per Key Trial
How much effort will it take to try each key? For very high security
applications it is best to assume a low value of effort. Even if it
would clearly take tens of thousands of computer cycles or more to
try a single key, there may be some pattern that enables huge blocks
of key values to be tested with much less effort per key. Thus it is
probably best to assume no more than a couple hundred cycles per key.
(There is no clear lower bound on this as computers operate in
parallel on a number of bits and a poor encryption algorithm could
allow many keys or even groups of keys to be tested in parallel.
However, we need to assume some value and can hope that a reasonably
strong algorithm has been chosen for our hypothetical high security
task.)
If the adversary can command a highly parallel processor or a large
network of work stations, 10^11 cycles per second is probably a
minimum assumption for availability today. Looking forward a few
years, there should be at least an order of magnitude improvement.
Thus assuming 10^10 keys could be checked per second or 3.6*10^12 per
hour or 6*10^14 per week or 2.4*10^15 per month is reasonable. This
implies a need for a minimum of 63 bits of randomness in keys to be
sure they cannot be found in a month. Even then it is possible that,
a few years from now, a highly determined and resourceful adversary
could break the key in 2 weeks (on average they need try only half
the keys).
These questions are considered in detail in "Minimal Key Lengths for
Symmetric Ciphers to Provide Adequate Commercial Security: A Report
by an Ad Hoc Group of Cryptographers and Computer Scientists"
[KeyStudy] which was sponsored by the Business Software Alliance. It
concluded that a reasonable key length in 1995 for very high security
is in the range of 75 to 90 bits and, since the cost of cryptography
does not vary much with they key size, recommends 90 bits. To update
these recommendations, just add 2/3 of a bit per year for Moore's law
D. Eastlake, J. Schiller, S. Crocker [Page 33]
INTERNET DRAFT Randomness Requirements for Security June 2004
[MOORE]. Thus, in the year 2004, this translates to a determination
that a reasonable key length is in the 81 to 96 bit range. In fact,
today, it is increasingly common to use keys longer than 96 bits,
such as 128bit (or longer) keys with AES and keys with effective
lengths of 112bits using tripleDES.
8.2.2 Meet in the Middle Attacks
If chosen or known plain text and the resulting encrypted text are
available, a "meet in the middle" attack is possible if the structure
of the encryption algorithm allows it. (In a known plain text attack,
the adversary knows all or part of the messages being encrypted,
possibly some standard header or trailer fields. In a chosen plain
text attack, the adversary can force some chosen plain text to be
encrypted, possibly by "leaking" an exciting text that would then be
sent by the adversary over an encrypted channel.)
An oversimplified explanation of the meet in the middle attack is as
follows: the adversary can halfencrypt the known or chosen plain
text with all possible first halfkeys, sort the output, then half
decrypt the encoded text with all the second halfkeys. If a match is
found, the full key can be assembled from the halves and used to
decrypt other parts of the message or other messages. At its best,
this type of attack can halve the exponent of the work required by
the adversary while adding a very large but roughly constant factor
of effort. Thus, if this attack can be mounted, a doubling of the
amount of randomness in the very strong key to a minimum of 192 bits
(96*2) is required for the year 2004 based on the [KeyStudy]
analysis.
This amount of randomness is well beyond the limit of that in the
inputs recommended by the US DoD for password generation and could
require user typing timing, hardware random number generation, or
other sources.
The meet in the middle attack assumes that the cryptographic
algorithm can be decomposed in this way but we can not rule that out
without a deep knowledge of the algorithm. Even if a basic algorithm
is not subject to a meet in the middle attack, an attempt to produce
a stronger algorithm by applying the basic algorithm twice (or two
different algorithms sequentially) with different keys may gain less
added security than would be expected. Such a composite algorithm
would be subject to a meet in the middle attack.
Enormous resources may be required to mount a meet in the middle
attack but they are probably within the range of the national
security services of a major nation. Essentially all nations spy on
other nations traffic.
D. Eastlake, J. Schiller, S. Crocker [Page 34]
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8.2.3 Other Considerations
[KeyStudy] also considers the possibilities of special purpose code
breaking hardware and having an adequate safety margin.
If the two parties agree on a key by DiffieHellman exchange [DH],
then in principle only half of this randomness would have to be
supplied by each party. However, there is probably some correlation
between their random inputs so it is probably best to assume you end
up with more like one and a half times the bits of randomness each
provides for very high security if DiffieHellman is used.
It should be noted that key length calculations such at those above
are controversial and depend on various assumptions about the
cryptographic algorithms in use. In some cases, a professional with a
deep knowledge of code breaking techniques and of the strength of the
algorithm in use could be satisfied with less than half of the 192
bit key size derived above.
For further examples of conservative design principles see
[FERGUSON].
D. Eastlake, J. Schiller, S. Crocker [Page 35]
INTERNET DRAFT Randomness Requirements for Security June 2004
9. Conclusion
Generation of unguessable "random" secret quantities for security use
is an essential but difficult task.
Hardware techniques to produce such randomness would be relatively
simple. In particular, the volume and quality would not need to be
high and existing computer hardware, such as disk drives, can be
used.
Widely available computational techniques are available to process
low quality random quantities from multiple sources or a larger
quantity of such low quality input from one source and produce a
smaller quantity of higher quality keying material. In the absence of
hardware sources of randomness, a variety of user and software
sources can frequently, with care, be used instead; however, most
modern systems already have hardware, such as disk drives or audio
input, that could be used to produce high quality randomness.
Once a sufficient quantity of high quality seed key material (a
couple of hundred bits) is available, computational techniques are
available to produce cryptographically strong sequences of
unpredictable quantities from this seed material.
D. Eastlake, J. Schiller, S. Crocker [Page 36]
INTERNET DRAFT Randomness Requirements for Security June 2004
10. Security Considerations
The entirety of this document concerns techniques and recommendations
for generating unguessable "random" quantities for use as passwords,
cryptographic keys, initialization vectors, sequence numbers, and
similar security uses.
11. Intellectual Property Considerations
By submitting this InternetDraft, I certify that any applicable
patent or other IPR claims of which I am aware have been disclosed,
and any of which I become aware will be disclosed, in accordance with
RFC 3668.
The IETF takes no position regarding the validity or scope
of any Intellectual Property Rights or other rights that might be
claimed to pertain to the implementation or use of the technology
described in this document or the extent to which any license under
such rights might or might not be available; nor does it represent
that it has made any independent effort to identify any such rights.
Information on the procedures with respect to rights in RFC documents
can be found in BCP 78 and BCP 79.
Copies of IPR disclosures made to the IETF Secretariat and any
assurances of licenses to be made available, or the result of an
attempt made to obtain a general license or permission for the use of
such proprietary rights by implementers or users of this
specification can be obtained from the IETF online IPR repository at
http://www.ietf.org/ipr.
The IETF invites any interested party to bring to its attention any
copyrights, patents or patent applications, or other proprietary
rights that may cover technology that may be required to implement
this standard. Please address the information to the IETF at ietf
ipr@ietf.org.
12. Copyright and Disclaimer
Copyright (C) The Internet Society 2004. This document is subject
to the rights, licenses and restrictions contained in BCP 78, and
except as set forth therein, the authors retain all their rights.
This document and the information contained herein are provided on an
"AS IS" basis and THE CONTRIBUTOR, THE ORGANIZATION HE/SHE REPRESENTS
OR IS SPONSORED BY (IF ANY), THE INTERNET SOCIETY AND THE INTERNET
ENGINEERING TASK FORCE DISCLAIM ALL WARRANTIES, EXPRESS OR IMPLIED,
INCLUDING BUT NOT LIMITED TO ANY WARRANTY THAT THE USE OF THE
INFORMATION HEREIN WILL NOT INFRINGE ANY RIGHTS OR ANY IMPLIED
WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.
D. Eastlake, J. Schiller, S. Crocker [Page 37]
INTERNET DRAFT Randomness Requirements for Security June 2004
13. Appendix A: Changes from RFC 1750
1. Additional acknowledgements have been added.
2. Insertion of section 5.2.4 on deskewing with Sboxes.
3. Addition of section 5.4 on Ring Oscillator randomness sources.
4. AES and the members of the SHA series producing more than 160
bits have been added. Use of AES has been emphasized and the use
of DES deemphasized.
5. Addition of section 6.3.3 on entropy pool techniques.
6. Addition of section 7.3 on the pseudorandom number generation
techniques given in FIPS 1862, 7.4 on those given in X9.82, and
section 7.5 on the random number generation techniques of the
/dev/random device in Linux and other UNIX systems.
7. Addition of references to the "Minimal Key Lengths for Symmetric
Ciphers to Provide Adequate Commercial Security" study published
in January 1996 [KeyStudy].
8. Minor wording changes and reference updates.
D. Eastlake, J. Schiller, S. Crocker [Page 38]
INTERNET DRAFT Randomness Requirements for Security June 2004
14. Informative References
[AES]  "Specification of the Advanced Encryption Standard (AES)",
United States of America, US National Institute of Standards and
Technology, FIPS 197, November 2001.
[ASYMMETRIC]  "Secure Communications and Asymmetric Cryptosystems",
edited by Gustavus J. Simmons, AAAS Selected Symposium 69, Westview
Press, Inc.
[BBS]  "A Simple Unpredictable PseudoRandom Number Generator", SIAM
Journal on Computing, v. 15, n. 2, 1986, L. Blum, M. Blum, & M. Shub.
[BRILLINGER]  "Time Series: Data Analysis and Theory", HoldenDay,
1981, David Brillinger.
[CRC]  "C.R.C. Standard Mathematical Tables", Chemical Rubber
Publishing Company.
[DAVIS]  "Cryptographic Randomness from Air Turbulence in Disk
Drives", Advances in Cryptology  Crypto '94, SpringerVerlag Lecture
Notes in Computer Science #839, 1984, Don Davis, Ross Ihaka, and
Philip Fenstermacher.
[DES]  "Data Encryption Standard", US National Institute of
Standards and Technology, FIPS 463, October 1999.
 "Data Encryption Algorithm", American National Standards
Institute, ANSI X3.921981.
(See also FIPS 112, Password Usage, which includes FORTRAN
code for performing DES.)
[DH]  RFC 2631, "DiffieHellman Key Agreement Method", Eric
Rescrola, June 1999.
[DNSSEC]  RFC 2535, "Domain Name System Security Extensions", D.
Eastlake, March 1999.
[DoD]  "Password Management Guideline", United States of America,
Department of Defense, Computer Security Center, CSCSTD00285.
(See also FIPS 112, Password Usage, which incorporates CSCSTD00285
as one of its appendices.)
[DSS]  "Digital Signature Standard (DSS)", US National Institute of
Standards and Technology, FIPS 1862, January 2000.
[FERGUSON]  "Practical Cryptography", Niels Ferguson and Bruce
Schneier, Wiley Publishing Inc., ISBN 047122894X, April 2003.
[GIFFORD]  "Natural Random Number", MIT/LCS/TM371, David K.
Gifford, September 1988.
D. Eastlake, J. Schiller, S. Crocker [Page 39]
INTERNET DRAFT Randomness Requirements for Security June 2004
[IEEE 802.11i]  "Amendment to Standard for Telecommunications and
Information Exchange Between Systems  LAN/MAN Specific Requirements
 Part 11: Wireless Medium Access Control (MAC) and physical layer
(PHY) specifications: Medium Access Control (MAC) Security
Enhancements", The Institute for Electrical and Electronics
Engineers, January 2004.
[IPSEC]  RFC 2401, "Security Architecture for the Internet
Protocol", S. Kent, R. Atkinson, November 1998.
[KAUFMAN]  "Network Security: Private Communication in a Public
World", Charlie Kaufman, Radia Perlman, and Mike Speciner, Prentis
Hall PTR, ISBN 0130460192, 2nd Edition 2002.
[KeyStudy]  "Minimal Key Lengths for Symmetric Ciphers to Provide
Adequate Commercial Security: A Report by an Ad Hoc Group of
Cryptographers and Computer Scientists", M. Blaze, W. Diffie, R.
Rivest, B. Schneier, T. Shimomura, E. Thompson, and M. Weiner,
January 1996, .
[KNUTH]  "The Art of Compute谩抿妊脴抿脿氓孺冕脿庙孺么脳葼r Programming", Volume 2: Seminumerical
Algorithms, Chapter 3: Random Numbers. Addison Wesley Publishing
Company, 3rd Edition November 1997, Donald E. Knuth.
[KRAWCZYK]  "How to Predict Congruential Generators", Journal of
Algorithms, V. 13, N. 4, December 1992, H. Krawczyk
[MAIL PEM]  RFCs 1421 through 1424:
 RFC 1421, Privacy Enhancement for Internet Electronic Mail:
Part I: Message Encryption and Authentication Procedures, 02/10/1993,
J. Linn
 RFC 1422, Privacy Enhancement for Internet Electronic Mail:
Part II: CertificateBased Key Management, 02/10/1993, S. Kent
 RFC 1423, Privacy Enhancement for Internet Electronic Mail:
Part III: Algorithms, Modes, and Identifiers, 02/10/1993, D. Balenson
 RFC 1424, Privacy Enhancement for Internet Electronic Mail:
Part IV: Key Certification and Related Services, 02/10/1993, B.
Kaliski
[MAIL PGP]
 RFC 2440, "OpenPGP Message Format", J. Callas, L.
Donnerhacke, H. Finney, R. Thayer", November 1998.
 RFC 3156, "MIME Security with OpenPGP" M. Elkins, D. Del
Torto, R. Levien, T. Roessler, August 2001.
[MAIL S/MIME]  RFCs 2632 through 2634:
 RFC 2632, "S/MIME Version 3 Certificate Handling", B.
Ramsdell, Ed., June 1999.
 RFC 2633, "S/MIME Version 3 Message Specification", B.
Ramsdell, Ed., June 1999.
D. Eastlake, J. Schiller, S. Crocker [Page 40]
INTERNET DRAFT Randomness Requirements for Security June 2004
 RFC 2634, "Enhanced Security Services for S/MIME" P.
Hoffman, Ed., June 1999.
[MD4]  "The MD4 MessageDigest Algorithm", RFC1320, April 1992, R.
Rivest
[MD5]  "The MD5 MessageDigest Algorithm", RFC1321, April 1992, R.
Rivest
[MODES]  "DES Modes of Operation", US National Institute of
Standards and Technology, FIPS 81, December 1980.
 "Data Encryption Algorithm  Modes of Operation", American
National Standards Institute, ANSI X3.1061983.
[MOORE]  Moore's Law: the exponential increase in the logic density
of silicon circuits. Originally formulated by Gordon Moore in 1964 as
a doubling every year starting in 1962, in the late 1970s the rate
fell to a doubling every 18 months and has remained there through the
date of this document. See "The New Hacker's Dictionary", Third
Edition, MIT Press, ISBN 0262181789, Eric S. Raymond, 1996.
[ORMAN]  "Determining Strengths For Public Keys Used For Exchanging
Symmetric Keys", draftormanpublickeylengths*.txt, Hilarie Orman,
Paul Hoffman, work in progress.
[RFC 1750]  "Randomness Requirements for Security", D. Eastlake, S.
Crocker, J. Schiller, December 1994.
[RSA BULL1]  "Suggestions for Random Number Generation in Software",
RSA Laboratories Bulletin #1, January 1996.
[RSA BULL13]  "A CostBased Security Analysis of Symmetric and
Asymmetric Key Lengths", RSA Laboratories Bulletin #13, Robert
Silverman, April 2000 (revised November 2001).
[SBOX1]  "Practical sbox design", S. Mister, C. Adams, Selected
Areas in Cryptography, 1996.
[SBOX2]  "Perfect Nonlinear Sboxes", K. Nyberg, Advances in
Cryptography  Eurocrypt '91 Proceedings, SpringerVerland, 1991.
[SCHNEIER]  "Applied Cryptography: Protocols, Algorithms, and Source
Code in C", 2nd Edition, John Wiley & Sons, 1996, Bruce Schneier.
[SHANNON]  "The Mathematical Theory of Communication", University of
Illinois Press, 1963, Claude E. Shannon. (originally from: Bell
System Technical Journal, July and October 1948)
[SHIFT1]  "Shift Register Sequences", Aegean Park Press, Revised
Edition 1982, Solomon W. Golomb.
D. Eastlake, J. Schiller, S. Crocker [Page 41]
INTERNET DRAFT Randomness Requirements for Security June 2004
[SHIFT2]  "Cryptanalysis of ShiftRegister Generated Stream Cypher
Systems", Aegean Park Press, 1984, Wayne G. Barker.
[SHA1]  "Secure Hash Standard (SHA1)", US National Institute of
Science and Technology, FIPS 1801, April 1993.
 RFC 3174, "US Secure Hash Algorithm 1 (SHA1)", D. Eastlake,
P. Jones, September 2001.
[SHA2]  "Secure Hash Standard", Draft (SHA2156/384/512), US
National Institute of Science and Technology, FIPS 1802, not yet
issued.
[SSH]  draftietfsecsh*, work in progress.
[STERN]  "Secret Linear Congruential Generators are not
Cryptographically Secure", Proceedings of IEEE STOC, 1987, J. Stern.
[TLS]  RFC 2246, "The TLS Protocol Version 1.0", T. Dierks, C.
Allen, January 1999.
[USENET]  RFC 977, "Network News Transfer Protocol", B. Kantor, P.
Lapsley, February 1986.
 RFC 2980, "Common NNTP Extensions", S. Barber, October
2000.
[VON NEUMANN]  "Various techniques used in connection with random
digits", von Neumann's Collected Works, Vol. 5, Pergamon Press, 1963,
J. von Neumann.
[X9.17]  "American National Standard for Financial Institution Key
Management (Wholesale)", American Bankers Association, 1985.
[X9.82]  "Random Number Generation", ANSI X9F1, work in progress.
D. Eastlake, J. Schiller, S. Crocker [Page 42]
INTERNET DRAFT Randomness Requirements for Security June 2004
Authors Addresses
Donald E. Eastlake 3rd
Motorola Laboratories
155 Beaver Street
Milford, MA 01757 USA
Telephone: +1 5087867554 (w)
+1 5086342066 (h)
EMail: Donald.Eastlake@motorola.com
Jeffrey I. Schiller
MIT, Room E40311
77 Massachusetts Avenue
Cambridge, MA 021394307 USA
Telephone: +1 6172530161
Email: jis@mit.edu
Steve Crocker
EMail: steve@stevecrocker.com
File Name and Expiration
This is file drafteastlakerandomness207.txt.
It expires December 2004.
D. Eastlake, J. Schiller, S. Crocker [Page 43]