Network Working Group Donald E. Eastlake, 3rd
INTERNETDRAFT Huawei
Intended status: Best Current Practice Steve Crocker
Obsoletes: 4086 Shinkuro
Charlie Kaufman
Microsoft
Jeffrey I. Schiller
MIT
Expires: 4 May 2014 5 November 2013
Randomness Requirements for Security
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 values. The use of pseudorandom
processes to generate secret quantities can result in pseudo
security. For example, the sophisticated attacker of these security
systems may find it easier to reproduce the environment that produced
the secret quantities, searching a 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 can be surprisingly difficult. This document points out
many pitfalls in using poor entropy sources or traditional pseudo
random number generation techniques for generating such quantities.
It recommends the use of multiple sources with a strong mixing
function, so that no single source need be fully trusted, and
provides techniques for extending a random seed to a larger quantity
of pseudorandom material in a cryptographically secure way. And it
gives examples of how large such quantities need to be for some
applications. This document obsoletes RFC 4086.
Status of This Document
This InternetDraft is submitted to IETF in full conformance with the
provisions of BCP 78 and BCP 79. This document is intended to be a
Best Current Practice. Comments should be sent to the authors.
Distribution is unlimited.
InternetDrafts are working documents of the Internet Engineering
Task Force (IETF), its areas, and its working groups. Note that
other groups may also distribute working documents as Internet
Drafts.
D. Eastlake, et al [Page 1]
INTERNET DRAFT Randomness Requirements for Security
InternetDrafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use InternetDrafts as reference
material or to cite them other than as "work in progress."
The list of current InternetDrafts can be accessed at
http://www.ietf.org/1idabstracts.html. The list of InternetDraft
Shadow Directories can be accessed at
http://www.ietf.org/shadow.html.
Acknowledgements
The following other persons (in alphabetic order) have also
contributed substantially to this document:
tbd
Special thanks to Paul Hoffman and John Kelsey for their extensive
comments on [RFC4086] and to Peter Gutmann, who has permitted the
incorporation of material from his paper "Software Generation of
Practically Strong Random Numbers".
The following persons (in alphabetic order) contributed to RFC 1750
and/or [RFC4086] the predecessors of this document. [RFC4086]
obsoleted RFC 1750.
David M. Balenson, Steve Bellovin, Daniel Brown, Don T. Davis,
Carl Ellison, Peter Gutmann, Neil Haller, Tony Hansen, Sandy
Harris, Paul Hoffman, Scott Hollenback, Marc Horowitz, Russ
Housley, Christian Huitema, Charlie Kaufman, John Kelsey, Steve
Kent, Hal Murray, Mats Naslund, Richard Pitkin, Damir Rajnovic,
Tim Redmond, and Doug Tygar.
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1. Introduction and Overview...............................5
2. General Requirements....................................6
3. Entropy Sources.........................................9
3.1 Volume Required........................................9
3.2 Existing Hardware Can Be Used For Randomness..........10
3.2.1 Using Existing Sound/Video Input....................10
3.2.2 Using Existing Disk Drives..........................10
3.2.3 On Chip Random Sources..............................11
3.3 Ring Oscillator Sources...............................11
3.4 Problems with Clocks and Serial Numbers...............12
3.5 Timing and Value of External Events...................13
3.6 NonHardware Sources of Randomness....................14
4. Deskewing.............................................15
4.1 Using Stream Parity to DeSkew........................15
4.2 Using Transition Mappings to DeSkew..................16
4.3 Using FFT to DeSkew..................................17
4.4 Using Compression to DeSkew..........................18
5. Mixing.................................................19
5.1 A Trivial Mixing Function.............................19
5.2 Stronger Mixing Functions.............................20
5.3 Using SBoxes for Mixing..............................22
5.4 DiffieHellman as a Mixing Function...................22
5.5 Using a Mixing Function to Stretch Random Bits........22
5.6 Other Factors in Choosing a Mixing Function...........23
6. Pseudo Random Number Generators........................24
6.1 Some Bad Ideas........................................24
6.1.1 The Fallacy of Complex Manipulation.................24
6.1.2 The Fallacy of Selection from a Large Database......25
6.1.3. Traditional PseudoRandom Sequences................25
6.2 Cryptographically Strong Sequences....................27
6.2.1 OFB and CTR Sequences...............................28
6.2.2 The Blum Blum Shub Sequence Generator...............29
6.3 Entropy Pool Techniques...............................30
7. Randomness Generation Examples and Standards...........32
7.1 Randomness Generators.................................32
7.1.1 US DoD Recommendations for Password Generation......32
7.1.2 The /dev/random Device..............................33
7.1.3 Windows CryptGenRandom..............................34
7.2 Generators Assuming a Source of Entropy...............35
7.2.1 X9.82 PseudoRandom Number Generation...............35
7.2.1.1 Notation..........................................35
7.1.2.2 Initializing the Generator........................36
7.1.2.5 Generating Random Bits............................36
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7.2.2 X9.17 Key Generation................................36
7.2.3 DSS PseudoRandom Number Generation.................37
8. Examples of Randomness Required........................39
8.1 Password Generation..................................39
8.2 A Very High Security Cryptographic Key................40
8.2.1 Effort per Key Trial................................40
8.2.2 Meet in the Middle Attacks..........................41
8.2.3 Other Considerations................................42
9. Conclusion.............................................43
10. Security Considerations...............................44
11. IANA Considerations...................................44
Informative References....................................45
Appendix A: Changes from [RFC4086]........................51
Author's Addresses........................................52
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1. Introduction and Overview
Cryptography is coming into wider use and is continuing to spread,
although there is a long way to go until it becomes ubiquitous.
Systems like SIDR, SSH [RFC4251], TLS [RFC5246], IP Security
[RFC4301], S/MIME, DNS Security [DNSSEC], Kerberos, etc. are maturing
and becoming a part of the network landscape [SIDR, MAIL*].
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.
Facilities for generating such random numbers, that is, the
availability of truly unpredictable sources, is spotty and in some
cases the quality is questionable. And even when the quality is, in
theory, excellent, there is always the risk that the facilities may
have been corrupted by and adversary. For example, there have been
indications that nation states have corrupted hardware random number
generators.
This is open wound in the design of cryptographic systems and
software. For the developer who wants to build a key or password
generation procedure that runs on a wide range of systems, this can
be a real problem.
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. Sometimes such pseudorandom
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
systems combine inputs from a number of potentially good randomness
sources, including hardware based random number sources. 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.
There are even TCP/IP protocol uses for randomness in picking initial
sequence numbers [RFC6528].
In some cases, such as the use of symmetric encryption with the one
time pads or an algorithm like the US 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]. Such
algorithms, with a high requirement for good randomness generation,
should be avoided and some believe that this weakness in DSA was
introduced to make it easier to break based on the use of poor random
numbers. 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 and, in fact, in the [VENONA]
project, old messages encrypted with poor quality or reused "one
time" pads have been broken. [SCHNEIER, FERGUSON, KAUFMAN]
In most of these cases, an adversary can try to determine a "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
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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 an information theoretic sense
[SHANNON]. This depends on the number of different secret values
possible and the probability of each value as follows:

\
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.
While the above analysis is correct on average, it can be misleading
in some cases for cryptographic analysis where what is really
important is the work factor for an adversary. For example, assume
that there was a pseudorandom number generator generating 128 bit
keys, as in the previous paragraph, but that it generated 0 half of
the time and a random selection from the remaining 2**128  1 values
the rest of the time. The Shannon equation above says that there are
64 bits of information in one of these key values but an adversary,
by simply trying the values 0, can break the security of half of the
uses, albeit a random half. Thus for cryptographic purposes, it is
also useful to look at other measures, such as minentropy, defined
as
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Minentropy =  log ( maximum ( p ) )
i
where i is as above. Using this equation, we get 1 bit of min
entropy for our new hypothetical distribution as opposed to 64 bits
of classical Shannon entropy.
A continuous spectrum of entropies, sometimes called Renyi entropy,
have been defined, specified by a parameter r. When r = 1, it is
Shannon entropy, and with r = infinity, it is minentropy. When r =
0, it is just log (n) where n is the number of nonzero
probabilities. Renyi entropy is a nonincreasing function of r, so
minentropy is always the most conservative measure of entropy and
usually the best to use for cryptographic evaluation. [LUBY]
Statistically tested randomness in the traditional sense 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] As another example, using AES
to encrypt successive integers such as 1, 2, 3 ... with a known key
will also produce output that has excellent statistical randomness
properties but is also predictable. On the other hand, taking
successive rolls of a sixsided die and encoding the resulting values
in ASCII would produce statistically poor output with a substantial
unpredictable component. So you should keep in mind that passing or
failing statistical tests doesn't tell you that something is
unpredictable or predictable.
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3. Entropy Sources
Entropy sources tend to be implementation dependent. Once one has
gathered sufficient entropy it can be used as the seed to produce the
required amount of cryptographically strong pseudorandomness, as
described in Sections 6 and 7, after being deskewed and/or mixed if
necessary as described in Sections 4 and 5.
Is there any hope for true strong portable randomness in the future?
There might be. In theory, 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 should 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. Most audio (or video) input
devices are useable [TURBID]. 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
3.3). 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.
ANSI X9 is currently developing a standard that includes a part
devoted to entropy sources. See [X9.82  Part 2].
3.1 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 6.2. 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 hardwarebased process is
likely to be much faster.
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3.2 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.
3.2.1 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. This method is very hardware and implementation
dependent.
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 lowlevel 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 (see Section 4) one can,
in UNIXese, generate a huge amount of medium quality random data by
doing
cat /dev/audio  compress  >randombitsfile
A detailed examination of this type of randomness source appears in
[TURBID].
3.2.2 Using Existing Disk Drives
Disk drives have small random fluctuations in their rotational speed
due to chaotic air turbulence [DAVIS, Jakobsson]. 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 described in 5.2 below. Nevertheless experimentation
over 15 years ago showed that, with such processing, even slow disk
drives on the slower computers of that day could easily produce 100
bits a minute or more of excellent random data.
Every increase in processor speed, which increases the resolution
with which disk motion can be timed, or increase in the rate of disk
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seeks, increases the rate of random bit generation possible with this
technique. At the time of [RFC4086] (2005) and using modern hardware,
a more typical rate of random bit production would be in excess of
10,000 bits a second. This technique is used in many operating system
library random number generators.
Note: the inclusion of cache memories in disk controllers has little
effect on this technique if very short seek times, which represent
cache hits, are simply ignored.
It is important to ensure you are using a true spinning disk drive.
Many modern computers come equipped with Solid State Disk Drives
(SSDs) which have no moving parts. With no moving parts there is no
spinning disk to provide the random fluctuations.
3.2.3 On Chip Random Sources
Some modern processors contain an onchip hardware random number
generators. For example newer Intel processors include a "rdrand"
instruction that provides random data.
Because exactly how this randomness is derived is not always
disclosed by the hardware manufacturer, it should not be relied upon
as the sole source of entropy for sensitive applications.
In theory onchip generators can provide a high speed source of
entropy. As such they are ideal for situations where cryptographic
strength is not essential, for example choosing TCP starting segment
numbers and similar protocol nonces.
3.3 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
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disk rotation timings must be (see Section 4). 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 Std. 802.112012 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. [IEEE802.11]
\ \ \
+> >0> >0 19 total  >0++
 / / /  
  
++ V
++
\ \ \   output
+> >0> >0 23 total  >0+> XOR >
 / / /   
  ++
++ ^ ^
 
\ \ \  
+> >0> >0 29 total  >0++ 
 / / /  
  
++ 

other randomness if available+
3.4 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
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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 MAC 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 due to the use
of their OUI (Organizationally Unique Identifier [RFC7042]) as upper
bits of the MAC address.
Problems such as those described above related to clocks and serial
numbers make code to produce unpredictable quantities difficult if
the code is to be ported across a variety of computer platforms and
systems.
3.5 Timing and Value of External Events
It is possible to measure the timing and content of mouse movement,
keystrokes, 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 and
lengths, can also be used, but only with care. In particular, the
possibility of manipulation of such network traffic measurements by
an adversary and the lack of history at system start up must be
carefully considered. If this input is subject to manipulation, it
must not be trusted as a source of entropy.
Indeed, almost any external sensor, such as raw radio reception or
temperature sensing in appropriately equipped computers, can be used
in principle. But in each case careful consideration must be given to
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how much such data is subject to adversarial manipulation and to how
much entropy it can actually provide.
The above techniques are quite powerful against any attackers having
no access to the quantities being measured. For example, they would
be powerful against offline attackers who had no access to your
environment and were trying to crack your random seed after the fact.
In all cases, the more accurately you can measure the timing or value
of an external sensor, the more rapidly you can generate bits.
3.6 NonHardware Sources of Randomness
The best single source of input entropy would be a hardware
randomness such as ring oscillators, disk drive timing, thermal
noise, or radioactive decay. However, there are other possibilities
which can be used instead or can be mixed with hardware randomness.
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 limited these non
hardware sources can 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 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.
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4. Deskewing
Is there any specific requirement on the shape of the distribution of
quantities gathered for the entropy to produce the random numbers?
The good news is the distribution need not be uniform. All that is
needed is a conservative estimate of how nonuniform it is to bound
performance. Simple techniques to deskew a bit stream are given
below and stronger cryptographic techniques are described in Section
5.2 below.
4.1 Using Stream Parity to DeSkew
As a simple but not particularly practical example, 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) ].
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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.)
The following table gives the length of the string that 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. But, as we shall see in section 6.1.2,
there are much stronger techniques that extract more of the available
entropy.
4.2 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:
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+++
 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
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 1s and 0s, it instead produces a
totally predictable sequence of exactly alternating 1s and 0s.
4.3 Using FFT to DeSkew
When real world data consists of strongly correlated bits, it may
still contain useful amounts of entropy. This entropy can be
extracted through use of various transforms, the most powerful of
which are described in section 5.2 below.
Using the Fourier transform of the data or its optimized variant, the
FFT, is an technique interesting primarily for theoretical reasons.
It can be show that this will discard strong correlations. If
adequate data is processed and remaining correlations decay, spectral
lines approaching statistical independence and normally distributed
randomness can be produced [BRILLINGER].
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4.4 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 Shannon's 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 in Section 5.2. At a minimum, the
beginning of the compressed sequence should be skipped and only later
bits used for applications requiring roughly random bits.
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5. Mixing
What is the best overall strategy for meeting the requirement for
unguessable random numbers? It is to obtain input from a number of
uncorrelated sources including hardware and to mix them with a strong
mixing function. Such a function will preserve the entropy present in
any of the sources even if other quantities being combined happen to
be fixed or easily guessable (low entropy). This is advisable even
with a theoretically good hardware source, as hardware can also fail
or the implementation of the hardware could have been corrupted by an
adversary with sufficient resources, for example a nation state.
Once you have used good sources, such as some of those listed in
Section 3, and mixed them as described in this section, you have a
strong seed. This can then be used to produce large quantities of
cryptographically strong material as described in Sections 6 and 7.
A strong mixing function is one which combines inputs and produces an
output where each output bit is a different complex nonlinear
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 that is more random, as discussed in Section 4 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 4.1 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 5.2. See
also [NASLUND].
5.1 A Trivial Mixing Function
A trivial example for single bit inputs described only for expository
purposes 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.
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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 4.1,
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.
5.2 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
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nonlinear function of all input bits. Other encryption functions
with this characteristic can also be used by considering them to mix
all of their key and data input bits.
Another good family of mixing functions are hashing functions such as
The US Government Secure Hash Standards [SHS] and newly selected
[KECCAK] series. These functions all take a practically unlimited
amount of input and produce a relatively short fixed length output
mixing all the input bits. (Previous RFCs on this topic also listed
the MD* series algorithms such as MD4 and MD5 [RFC1321] but their use
and the use of SHA1 (or SHA0) is no longer encouraged [RFC6151]
[RFC6194].)
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 and you want to employ
AES, 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.
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 4.1 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. Alternatively, for some
applications, you could use the entire hash output to retain almost
all of the 5+ bits of entropy in a 160 bit quantity.
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5.3 Using SBoxes for Mixing
Many block encryption functions, including AES, incorporate modules
known as SBoxes (substitution boxes). These produce a smaller number
of outputs from a larger number of inputs through a complex non
linear mixing function that 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
can be used for mixing. [SBOX]
5.4 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 the
parties [DH].
If these initial quantities are random and uncorrelated, then the
shared secret combines their entropy, but, of course, cannot produce
more randomness than the size of the shared secret generated.
While this is true if the DiffieHellman computation is performed
privately, an adversary that can observe either of the public keys
and knows the modulus being used need only search through the space
of the other secret key in order to be able to calculate the shared
secret [DH]. So, conservatively, it would be best to consider public
DiffieHellman to produce a quantity whose guessability corresponds
to the worst of the two inputs. Because of this and the fact that
DiffieHellman is computationally intensive, its use as a mixing
function is not recommended.
5.5 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
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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 4*12 or 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. Mixing to fewer
bits than are input will tend to strengthen the randomness of the
output.
The last table in Section 5.1 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 twobit 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.
5.6 Other Factors in Choosing a Mixing Function
For local use, AES and the SHA* family [SHS] (except for SHA0 and
SHA1 [RFC6194]) have the advantages that they have been widely
studied and tested for flaws and are widely documented and
implemented, with hardware and software implementations available all
over the world including open source code. The SHA* family for *>1
[RFC6234] tend to require more CPU cycles than AES. (The previous
version of this RFC suggested use of members of the MD* family of
hashes and SHA1 but this is no longer encouraged [RFC1321] [RFC3174]
[RFC6150] [RFC6151] [RFC6194].)
Where input lengths are unpredictable, hash algorithms are 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
accommodate 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, SHA*, MD*, or Keccak algorithms other than patents
for which an irrevocable royalty free worldwide license has been
granted. There may be patents of which the authors are unaware or
patents on implementations or uses or other relevant patents issued
or to be issued.
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6. Pseudo Random Number Generators
When you have a seed with sufficient entropy, from input as described
in Section 3 possibly deskewed and mixed as described in Sections 4
and 5, you can algorithmically extend that seed to produce a large
number of cryptographically strong random quantities. Such algorithms
are platform independent and can operate in the same fashion on any
computer. To be secure, their input(s) and internal workings must be
protected from adversarial observation.
The design of such pseudo random number generation algorithms, like
the design of symmetric encryption algorithms, is not a task for
amateurs. Section 6.1 below lists a number of bad ideas that failed
algorithms have used. If you are interested in what works, you can
skip 6.1 and just read from 6.2 including Section 7 below which
describes and gives references for some standard pseudo random number
generation algorithms. See Section 7 and [X9.82  Part 3].
6.1 Some Bad Ideas
The subsections below describe a number of idea that might seem
reasonable but which lead to insecure pseudo random number
generation.
6.1.1 The Fallacy of Complex Manipulation
One strategy that 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 entropy 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
numbers when there has been no theory behind or analysis of the
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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 entropy in a good seed!
6.1.2 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.
6.1.3. Traditional PseudoRandom Sequences
This section talks about traditional sources of deterministic of
"pseudorandom" numbers. These typically start with a "seed" quantity
and use simple numeric or logical operations to produce a sequence of
values. Note that none of the techniques discussed in this section is
suitable for cryptographic use. They are presented for general
information.
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[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. Sometimes they can
store the message they are trying to break and repeatedly attack it.
They are also 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 +>  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 taps
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 known for breaking all polynomial congruent generators.
[KRAWCZYK]
6.2 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
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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
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.)
6.2.1 OFB and CTR Sequences
One way to achieve a strong sequence is to have the values be
produced by taking a seed value and 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 successive integers as in
counter (CTR) mode encryption. Alternatively, you can feedback all of
the output value from encryption into the value to be encrypted for
the next iteration. This is a particular example of output feedback
mode (OFB). [MODES]
An example is shown below where shifting and masking are used to
combine part of the output feedback with part of the old input. This
type of partial feedback should be avoided for reasons described
below.
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++
 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
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
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.
6.2.2 The Blum Blum Shub Sequence Generator
Currently the generator that 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.
Its only disadvantage is that it is computationally intensive
compared with the traditional techniques give in 6.1.3 above. This is
not a major draw back if it is used for moderately infrequent
purposes, such as generating session keys.
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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
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 Entropy Pool Techniques
Many modern pseudorandom number sources, such as those describe in
Sections 7.1.2 and 7.1.3, utilize the technique of maintaining a
"pool" of bits and providing operations for strongly mixing input
with some randomness into the pool and extracting pseudo random bits
from the pool. This is illustrated in the figure below.
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++ ++ ++
> Mix In > POOL > Extract >
 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 [RSA BULL1] for similar
suggestions.
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7. Randomness Generation Examples and Standards
Several public standards and widely deployed examples are in place
for the generation of keys or other cryptographically random
quantities. Some, in section 7.1 below, include an entropy source.
Others, described in section 7.2, provide the pseudorandom number
strong sequence generator but assume the input of a random seed or
input from a source of entropy.
7.1 Randomness Generators
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 [DES]. The third is
a more modern and stronger standard based on SHA1 [SHS]. Lastly the
widely deployed modern UNIX and Windows random number generators are
described.
7.1.1 US DoD Recommendations for Password Generation
The United States Department of Defense has 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 the ASCII bytes for 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.
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7.1.2 The /dev/random Device
Several versions of the UNIX operating system provide a kernel
resident random number generator. In some cases, these generators
make 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 XORed 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 slowly in that
case.
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 [SHS]
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 and shutdown scripts save the pool to a disk file at shutdown
and read this file at system startup.
There are two userexported 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
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/dev/random device is suitable for key generation for longterm 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
equipped 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). [PGP])
7.1.3 Windows CryptGenRandom
Microsoft's recommendation to users of the widely deployed Windows
operating system is generally to use the CryptGenRandom pseudorandom
number generation call with the CryptAPI cryptographic service
provider. This takes a handle to a cryptographic service provider
library, a pointer to a buffer by which the caller can provide
entropy and into which the generated pseudorandomness is returned,
and an indication of how many octets of randomness are desired.
The Windows CryptAPI cryptographic service provider stores a seed
state variable with every user. When CryptGenRandom is called, this
is combined with any randomness provided in the call and various
system and user data such as the process ID, thread ID, system clock,
system time, system counter, memory status, free disk clusters, and
hashed user environment block. This data is all feed to SHA1 and the
output used to seed an RC4 key stream. That key stream is used to
produce the pseudorandom data requested and to update the user's
seed state variable.
Users of Windows ".NET" will probably find it easier to use the
RNGCryptoServiceProvider.GetBytes method interface.
For further information, see [WSC].
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7.2 Generators Assuming a Source of Entropy
The pseudorandom number generators described in the following three
sections all assume that a seed value with sufficient entropy is
provided to them. They then generate a strong sequence (see Section
6.2) from that seed.
7.2.1 X9.82 PseudoRandom Number Generation
The ANSI X9F1 committee is in the final stages of creating a standard
for random number generation covering both true randomness generators
and pseudorandom number generators. It includes a number of pseudo
random number generators based on hash functions one of which will
probably be based on HMAC SHA hash constructs [RFC2104]. The draft
version of this generated is as described below omitting a number of
optional features [X9.82].
In the description in the subsections below, the HMAC hash construct
is simply referred to as HMAC but, of course, in an particular use, a
particular standard SHA function must be selected. Generally
speaking, if the strength of the pseudorandom values to be generated
is to be N bits, the SHA function chosen must be one generating N or
more bits of output and a source of at least N bits of input entropy
will be required. The same hash function must be used throughout an
instantiation of this generator.
7.2.1.1 Notation
In the following sections the notation give below is used:
hash_length is the output size of the underlying hash function in
use.
input_entropy is the input bit string that provides entropy to the
generator.
K is a bit string of size hash_length that is part of the state of
the generator and is updated at least once each time random
bits are generated.
V is a bit string of size hash_length and is part of the state of
the generator that is updated each time hash_length bits of
output are generated.
 represents concatenation
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7.1.2.2 Initializing the Generator
Set V to all zero bytes except that the low order bit of each byte is
set to one.
Set K to all zero bytes.
K = HMAC ( K, V  0x00  input_entropy )
V = HMAC ( K, V )
K = HMAC ( K, V  0x01  input_entropy )
V = HMAC ( K, V )
Note: all SHA algorithms produce an integral number of bytes of the
length of K and V will be an integral number of bytes.
7.1.2.5 Generating Random Bits
When output is called for simply set
V = HMAC ( K, V )
and use leading bits from V. If more bits are needed than the length
of V, set "temp" to a null bit string and then repeatedly perform
V = HMAC ( K, V )
temp = temp  V
stopping as soon a temp is equal to or longer than the number of
random bits called for and use the called for number of leading bits
from temp. The definition of the algorithm prohibits calling from
more than 2**35 bits.
7.2.2 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
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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.
7.2.3 DSS PseudoRandom Number Generation
Appendix 3 of the NIST Digital Signature Standard [DSS] provides a
method of producing a sequence of pseudorandom 160 bit quantities
for use as private keys or the like. This has been modified by Change
Notice 1 [DSS CN1] to produce the following algorithm for generating
general purpose pseudorandom numbers:
t = 0x 67452301 EFCDAB89 98BADCFE 10325476 C3D2E1F0
XKEY = initial seed
0
For j = 0 to ...
XVAL = ( XKEY + optional user input ) (Mod 2^512)
j
X = G( t, XVAL )
j
XKEY = ( 1 + XKEY + X ) (Mod 2^512)
j+1 j j
The quantities X thus produced are the pseudorandom sequence of 160
bit values. Two functions can be used for "G" above. Each produces
a 160bit value and takes two arguments, the first argument a 160bit
value and the second a 512 bit value.
The first is based on SHA1 and works by setting the 5 linking
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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. [SHS]
As an alternative second method, NIST also defined an alternate G
function based on multiple applications of the DES encryption
function [DSS].
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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 threeword 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 fourword 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, et al [Page 39]
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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 reasonably hope that a
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, 10^13 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 10^13 keys could be checked per second or 3.6*10^15 per
hour or 6*10^17 per week or 2.4*10^18 per month is reasonable. This
implies a need for a minimum of 74 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, et al [Page 40]
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[MOORE]. Thus, in the year 2013, this translates to a determination
that a reasonable key length is in the 87 to 102 bit range. In fact,
today, it is increasingly common to use keys longer than 102 bits,
such as 128bit (or longer) keys with AES.
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 204 bits
(102*2) is required for the year 2013 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, and/or
other sources.
The meet in the middle attack assumes that the cryptographic
algorithm can be decomposed in this way. Hopefully no modern
algorithm has this weakness but there may be cases where we are not
sure of that or even of what algorithm a key will be used with. 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 will 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
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other nations traffic.
8.2.3 Other Considerations
[KeyStudy] also considers the possibilities of special purpose code
breaking hardware and having an adequate safety margin.
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 204
bit key size derived above.
For further examples of conservative design principles see
[FERGUSON].
D. Eastlake, et al [Page 42]
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9. Conclusion
Generation of unguessable "random" secret quantities for security use
is an essential but difficult task.
Hardware techniques to produce the needed entropy are relatively
simple. In particular, the volume and quality needed is not high and
existing computer hardware can be used. However, in an era when the
integrity of hardware design can be corrupted by nation states,
special purpose built in hardware random number generation should not
be trusted as the sole source of randomness.
Widely available computational techniques are available to process
random quantities from multiple sources, including low quality
sources, so as to produce a smaller quantity of higher quality keying
material. A variety of hardware, user, and software sources should be
used.
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
computationally unpredictable quantities from this seed material.
D. Eastlake, et al [Page 43]
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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. See earlier sections of this document.
11. IANA Considerations
This document requires no IANA actions. RFC Editor: Please delete
this section before publication.
D. Eastlake, et al [Page 44]
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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]
 Arends, R., Austein, R., Larson, M., Massey, D., and S. Rose,
"DNS Security Introduction and Requirements", RFC 4033,
March 2005.
 Arends, R., Austein, R., Larson, M., Massey, D., and S. Rose,
"Resource Records for the DNS Security Extensions", RFC
4034, March 2005.
 Arends, R., Austein, R., Larson, M., Massey, D., and S. Rose,
"Protocol Modifications for the DNS Security Extensions",
RFC 4035, March 2005.
[DoD]  "Password Management Guideline", United States of America,
Department of Defense, Computer Security Center, CSC
STD00285.
D. Eastlake, et al [Page 45]
INTERNET DRAFT Randomness Requirements for Security
(See also FIPS 112, Password Usage, which incorporates CSC
STD00285 as one of its appendices.)
[DSS]  "Digital Signature Standard (DSS)", US National Institute of
Standards and Technology, FIPS 1862, January 2000.
[DSS CN1]  "Digital Signature Standard Change Notice 1", US National
Institute of Standards and Technology, FIPS 1862 Change Notice
1, 5 October 2001.
[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.
[IEEE802.11]  IEEE Std 802.112012, "Wireless LAN Medium Access
Control (MAC) and physical layer (PHY) Specifications", 29
March 2012.
[Jakobsson]  M. Jakobsson, E. Shriver, B. K. Hillyer, and A. Juels,
"A practical secure random bit generator", Proceedings of the
Fifth ACM Conference on Computer and Communications Security,
1998. See also
http://citeseer.ist.psu.edu/article/jakobsson98practical.html.
[KAUFMAN]  "Network Security: Private Communication in a Public
World", Charlie Kaufman, Radia Perlman, and Mike Speciner,
Prentis Hall PTR, ISBN 0130460192, 2nd Edition 2002.
[KECCAK] 
http://csrc.nist.gov/groups/ST/hash/sha3/winner_sha3.html
http://keccak.noekeon.org
[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 Computer Programming", Volume 2: Seminumerical
Algorithms, Chapter 3: Random Numbers, Donald E. Knuth, Addison
Wesley Publishing Company, 3rd Edition November 1997.
[KRAWCZYK]  "How to Predict Congruential Generators", H. Krawczyk,
Journal of Algorithms, V. 13, N. 4, December 1992.
[LUBY]  "Pseudorandomness and Cryptographic Applications", Michael
Luby, Princeton University Press, ISBN 0691025460, 8 January
1996.
D. Eastlake, et al [Page 46]
INTERNET DRAFT Randomness Requirements for Security
[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]
 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.
 RFC 2634, "Enhanced Security Services for S/MIME" P. Hoffman,
Ed., June 1999.
[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.
[NASLUND]  "Extraction of Optimally Unbiased Bits from a Biased
Source", M. Naslund and A. Russell, IEEE Transactions on
Information Theory. 46(3), May 2000.
[ORMAN]  "Determining Strengths For Public Keys Used For Exchanging
Symmetric Keys", RFC 3766, Hilarie Orman, Paul Hoffman, April
2004.
[RFC1321]  "The MD5 MessageDigest Algorithm", RFC1321, April 1992,
R. Rivest
[RFC2104]  Krawczyk, H., Bellare, M., and R. Canetti, "HMAC: Keyed
Hashing for Message Authentication", RFC 2104, February 1997.
[RFC3174]  RFC 3174, "US Secure Hash Algorithm 1 (SHA1)", D.
Eastlake, P. Jones, September 2001.
[RFC4086]  "Randomness Requirements for Security", D. Eastlake, S.
Crocker, J. Schiller, June 2005. (Obsoleted by this document.)
[RFC4251]  Ylonen, T. and C. Lonvick, Ed., "The Secure Shell (SSH)
D. Eastlake, et al [Page 47]
INTERNET DRAFT Randomness Requirements for Security
Protocol Architecture", RFC 4251, January 2006.
[RFC4301]  Kent, S. and K. Seo, "Security Architecture for the
Internet Protocol", RFC 4301, December 2005.
[RFC5246]  Dierks, T. and E. Rescorla, "The Transport Layer
Security (TLS) Protocol Version 1.2", RFC 5246, August 2008.
[RFC6150]  Turner, S. and L. Chen, "MD4 to Historic Status",
RFC 6150, March 2011.
[RFC6151]  Turner, S. and L. Chen, "Updated Security Considerations
for the MD5 MessageDigest and the HMACMD5 Algorithms", RFC
6151, March 2011.
[RFC6194]  Polk, T., Chen, L., Turner, S., and P. Hoffman, "Security
Considerations for the SHA0 and SHA1 MessageDigest
Algorithms", RFC 6194, March 2011.
[RFC6234]  Eastlake 3rd, D. and T. Hansen, "US Secure Hash
Algorithms (SHA and SHAbased HMAC and HKDF)", RFC 6234, May
2011.
[RFC6528]  Gont, F. and S. Bellovin, "Defending against Sequence
Number Attacks", RFC 6528, February 2012.
[RFC7042]  Eastlake 3rd, D. and J. Abley, "IANA Considerations and
IETF Protocol and Documentation Usage for IEEE 802 Parameters",
BCP 141, RFC 7042, October 2013.
[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).
[SBOX]
 "Practical sbox design", S. Mister, C. Adams, Selected Areas
in Cryptography, 1996.
 "Perfect Nonlinear Sboxes", K. Nyberg, Advances in
Cryptography  Eurocrypt '91 Proceedings, SpringerVerland,
1991.
[SCHNEIER]  "Applied Cryptography: Protocols, Algorithms, and Source
Code in C", Bruce Schneier, 2nd Edition, John Wiley & Sons,
1996.
[SHANNON]  "The Mathematical Theory of Communication", University of
Illinois Press, 1963, Claude E. Shannon. (originally from:
D. Eastlake, et al [Page 48]
INTERNET DRAFT Randomness Requirements for Security
Bell System Technical Journal, July and October 1948)
[SHIFT]
 "Shift Register Sequences", Solomon W. Golomb, Aegean Park
Press, Revised Edition 1982.
 "Cryptanalysis of ShiftRegister Generated Stream Cypher
Systems", Wayne G. Barker, Aegean Park Press, 1984.
[SHS]  "Secure Hash Standard", US National Institute of Science and
Technology, FIPS 1804, March 2012.
[SIDR] 
[SP80090A]  "Recommendation for Random Number Generation Using
Deterministic Random Bit Generators", US National Institute of
Standards and Technology, Special Publication 80090A, January
2012.
[SP80090B]  "Recommendation for the Entropy Sources Used for Random
Bit Generation", US National Institute of Standards and
Technology, DRAFT Special Publication 80090B, August 2012.
[SP80090C]  "Recommendation for Random Bit Generator (RBG)
Construction", US National Institute of Standards and
Technology, DRAFT Special Publication 80090C, August 2012.
[STERN]  "Secret Linear Congruential Generators are not
Cryptographically Secure", J. Stern, Proceedings of IEEE STOC,
1987.
[TURBID]  "High Entropy Symbol Generator", John S. Denker,
, 2003.
[USENET]
 RFC 977, "Network News Transfer Protocol", B. Kantor, P.
Lapsley, February 1986.
 RFC 2980, "Common NNTP Extensions", S. Barber, October
2000.
[VENONA] 
[VON NEUMANN]  "Various techniques used in connection with random
digits", von Neumann's Collected Works, Vol. 5, Pergamon Press,
1963, J. von Neumann.
[WSC]  "Writing Secure Code, Second Edition", Michael Howard, David.
C. LeBlanc, Microsoft Press, ISBN 0735617228, December 2002.
[X9.17]  "American National Standard for Financial Institution Key
Management (Wholesale)", American Bankers Association, 1985.
D. Eastlake, et al [Page 49]
INTERNET DRAFT Randomness Requirements for Security
[X9.82]  "Random Number Generation", American National Standards
Institute, ANSI X9F1, work in progress.
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INTERNET DRAFT Randomness Requirements for Security
Appendix A: Changes from [RFC4086]
1. Deleted changes from RFC 1750. See [RFC4086] if you are
interested.
2. Eliminate any appearance of recommending MD* algorithms or SHA0
or SHA1 or DES.
3. Update many RFC and other references such as 802.11i2004 >
802.112012, ...
4. Add references such as [SIDR], ...
5. Update based on the revelations released by Edward J. Snowden.
Basically, these point to a much higher probability of nation
state sponsored corruption of hardware random number generators
or deterministic pseudorandom number generator standards. The
lesson is never trust one source of randomness.
6. Add references to NIST SP80090A, SP80090B, and SP80090C.
X. Substantial editorial changes
D. Eastlake, et al [Page 51]
INTERNET DRAFT Randomness Requirements for Security
Author's Addresses
Donald E. Eastlake 3rd
Huawei Technologies
155 Beaver Street
Milford, MA 01757 USA
Telephone: +1 5083332270
EMail: d3e3e3@gmail.com
Steve Crocker
Shinkuro
EMail: steve@stevecrocker.com
Charlie Kaufman
Microsoft
Email: charliek@microsoft.com>
Jeffrey I. Schiller
MIT, Room E17110A
77 Massachusetts Avenue
Cambridge, MA 021394307 USA
Telephone: +1 6179100259
Email: jis@mit.edu
D. Eastlake, et al [Page 52]
INTERNET DRAFT Randomness Requirements for Security
Copyright and IPR Provisions
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This document is subject to BCP 78 and the IETF Trust's Legal
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Contribution.
D. Eastlake, et al [Page 53]