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[dragonfly.git] / crypto / heimdal-0.6.3 / doc / standardisation / rfc1750.txt

Network Working Group                                   D. Eastlake, 3rd
Request for Comments: 1750                                           DEC
Category: Informational                                       S. Crocker
                                                               Cybercash
                                                             J. Schiller
                                                                     MIT
                                                           December 1994


                Randomness Recommendations for Security

Status of this Memo

   This memo provides information for the Internet community.  This memo
   does not specify an Internet standard of any kind.  Distribution of
   this memo is unlimited.

Abstract

   Security systems today are built on increasingly strong cryptographic
   algorithms that foil pattern analysis attempts. However, the security
   of these systems is dependent on generating secret quantities for
   passwords, cryptographic keys, and similar quantities.  The use of
   pseudo-random processes to generate secret quantities can result in
   pseudo-security.  The sophisticated attacker of these security
   systems may find it easier to reproduce the environment that produced
   the secret quantities, searching the resulting small set of
   possibilities, than to locate the quantities in the whole of the
   number space.

   Choosing random quantities to foil a resourceful and motivated
   adversary is surprisingly difficult.  This paper points out many
   pitfalls in using traditional pseudo-random number generation
   techniques for choosing such quantities.  It recommends the use of
   truly random hardware techniques and shows that the existing hardware
   on many systems can be used for this purpose.  It provides
   suggestions to ameliorate the problem when a hardware solution is not
   available.  And it gives examples of how large such quantities need
   to be for some particular applications.












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Acknowledgements

   Comments on this document that have been incorporated were received
   from (in alphabetic order) the following:

        David M. Balenson (TIS)
        Don Coppersmith (IBM)
        Don T. Davis (consultant)
        Carl Ellison (Stratus)
        Marc Horowitz (MIT)
        Christian Huitema (INRIA)
        Charlie Kaufman (IRIS)
        Steve Kent (BBN)
        Hal Murray (DEC)
        Neil Haller (Bellcore)
        Richard Pitkin (DEC)
        Tim Redmond (TIS)
        Doug Tygar (CMU)

Table of Contents

   1. Introduction........................................... 3
   2. Requirements........................................... 4
   3. Traditional Pseudo-Random Sequences.................... 5
   4. Unpredictability....................................... 7
   4.1 Problems with Clocks and Serial Numbers............... 7
   4.2 Timing and Content of External Events................  8
   4.3 The Fallacy of Complex Manipulation..................  8
   4.4 The Fallacy of Selection from a Large Database.......  9
   5. Hardware for Randomness............................... 10
   5.1 Volume Required...................................... 10
   5.2 Sensitivity to Skew.................................. 10
   5.2.1 Using Stream Parity to De-Skew..................... 11
   5.2.2 Using Transition Mappings to De-Skew............... 12
   5.2.3 Using FFT to De-Skew............................... 13
   5.2.4 Using Compression to De-Skew....................... 13
   5.3 Existing Hardware Can Be Used For Randomness......... 14
   5.3.1 Using Existing Sound/Video Input................... 14
   5.3.2 Using Existing Disk Drives......................... 14
   6. Recommended Non-Hardware Strategy..................... 14
   6.1 Mixing Functions..................................... 15
   6.1.1 A Trivial Mixing Function.......................... 15
   6.1.2 Stronger Mixing Functions.......................... 16
   6.1.3 Diff-Hellman as a Mixing Function.................. 17
   6.1.4 Using a Mixing Function to Stretch Random Bits..... 17
   6.1.5 Other Factors in Choosing a Mixing Function........ 18
   6.2 Non-Hardware Sources of Randomness................... 19
   6.3 Cryptographically Strong Sequences................... 19



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   6.3.1 Traditional Strong Sequences....................... 20
   6.3.2 The Blum Blum Shub Sequence Generator.............. 21
   7. Key Generation Standards.............................. 22
   7.1 US DoD Recommendations for Password Generation....... 23
   7.2 X9.17 Key Generation................................. 23
   8. Examples of Randomness Required....................... 24
   8.1  Password Generation................................. 24
   8.2 A Very High Security Cryptographic Key............... 25
   8.2.1 Effort per Key Trial............................... 25
   8.2.2 Meet in the Middle Attacks......................... 26
   8.2.3 Other Considerations............................... 26
   9. Conclusion............................................ 27
   10. Security Considerations.............................. 27
   References............................................... 28
   Authors' Addresses....................................... 30

1. Introduction

   Software cryptography is coming into wider use.  Systems like
   Kerberos, PEM, PGP, etc. are maturing and becoming a part of the
   network landscape [PEM].  These systems provide substantial
   protection against snooping and spoofing.  However, there is a
   potential flaw.  At the heart of all cryptographic systems is the
   generation of secret, unguessable (i.e., random) numbers.

   For the present, the lack of generally available facilities for
   generating such unpredictable numbers is an open wound in the design
   of cryptographic software.  For the software developer who wants to
   build a key or password generation procedure that runs on a wide
   range of hardware, the only safe strategy so far has been to force
   the local installation to supply a suitable routine to generate
   random numbers.  To say the least, this is an awkward, error-prone
   and unpalatable solution.

   It is important to keep in mind that the requirement is for data that
   an adversary has a very low probability of guessing or determining.
   This will fail if pseudo-random data is used which only meets
   traditional statistical tests for randomness or which is based on
   limited range sources, such as clocks.  Frequently such random
   quantities are determinable by an adversary searching through an
   embarrassingly small space of possibilities.

   This informational document suggests techniques for producing random
   quantities that will be resistant to such attack.  It recommends that
   future systems include hardware random number generation or provide
   access to existing hardware that can be used for this purpose.  It
   suggests methods for use if such hardware is not available.  And it
   gives some estimates of the number of random bits required for sample



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   applications.

2. Requirements

   Probably the most 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 re-usable passwords, it is desirable that users be
   able to remember the password.  This may make it advisable to use
   pronounceable character strings or phrases composed on ordinary
   words.  But this only affects the format of the password information,
   not the requirement that the password be very hard to guess.)

   Many other requirements come from the cryptographic arena.
   Cryptographic techniques can be used to provide a variety of services
   including confidentiality and authentication.  Such services are
   based on quantities, traditionally called "keys", that are unknown to
   and unguessable by an adversary.

   In some cases, such as the use of symmetric encryption with the one
   time pads [CRYPTO*] or the US Data Encryption Standard [DES], 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 [ASYMMETRIC, CRYPTO*].

   The frequency and volume of the requirement for random quantities
   differs greatly for different cryptographic systems.  Using pure RSA
   [CRYPTO*], random quantities are required when the key pair is
   generated, but thereafter any number of messages can be signed
   without any further need for randomness.  The public key Digital
   Signature Algorithm that has been proposed by the US National
   Institute of Standards and Technology (NIST) requires good random
   numbers for each signature.  And encrypting with a one time pad, in
   principle the strongest possible encryption technique, requires a
   volume of randomness equal to all the messages to be processed.

   In most of these cases, an adversary can try to determine the
   "secret" key by trial and error.  (This is possible as long as the
   key is enough smaller than the message that the correct key can be
   uniquely identified.)  The probability of an adversary succeeding at
   this must be made acceptably low, depending on the particular
   application.  The size of the space the adversary must search is
   related to the amount of key "information" present in the information
   theoretic sense [SHANNON].  This depends on the number of different



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   secret values possible and the probability of each value as follows:

                      -----
                       \
        Bits-of-info =  \  - p   * log  ( p  )
                        /     i       2    i
                       /
                      -----

   where i varies 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 non-negative.)

   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^(n-1) , 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 56 bit keys.
   If these 56 bit keys are derived by using a fixed pseudo-random
   number generator that is seeded with an 8 bit seed, then an adversary
   needs to search through only 256 keys (by running the pseudo-random
   number generator with every possible seed), not the 2^56 keys that
   may at first appear to be the case. Only 8 bits of "information" are
   in these 56 bit keys.

3. Traditional Pseudo-Random Sequences

   Most traditional sources of random numbers use deterministic sources
   of "pseudo-random" numbers.  These typically start with a "seed"
   quantity and use numeric or logical operations to produce a sequence
   of values.

   [KNUTH] has a classic exposition on pseudo-random 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,



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   perhaps unlimited, chances at guessing the correct value and should
   be assumed to be aided by a computer.

   For testing the "randomness" of numbers, Knuth suggests a variety of
   measures including statistical and spectral.  These tests check
   things like autocorrelation between different parts of a "random"
   sequence or distribution of its values.  They could be met by a
   constant stored random sequence, such as the "random" sequence
   printed in the CRC Standard Mathematical Tables [CRC].

   A typical pseudo-random number generation technique, known as a
   linear congruence pseudo-random number generator, is modular
   arithmetic where the N+1th value is calculated from the Nth value by

        V    = ( V  * a + b )(Mod c)
         N+1      N

   The above technique has a strong relationship to linear shift
   register pseudo-random 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:

      +----+     +----+     +----+                      +----+
      | 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 pseudo-random number generator algorithms
   is measured by statistical tests on such sequences.  Carefully chosen
   values of the initial V and a, b, and c or the placement of shift
   register tap in the above simple processes can produce excellent
   statistics.






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   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 pseudo-random number
   generator, the sequence may be determinable from observation of a
   short portion of the sequence [CRYPTO*, 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 pseudo-random values is released, the seed can be
   determined from short sequences.

   Not only have linear congruent generators been broken, but techniques
   are now known for breaking all polynomial congruent generators
   [KRAWCZYK].

4. Unpredictability

   Randomness in the traditional sense described in section 3 is NOT the
   same as the unpredictability required for security use.

   For example, use of a widely available constant sequence, such as
   that from the CRC tables, is very weak against an adversary. Once
   they learn of or guess it, they can easily break all security, future
   and past, based on the sequence [CRC].  Yet the statistical
   properties of these tables are good.

   The following sections describe the limitations of some randomness
   generation techniques and sources.

4.1 Problems with Clocks and Serial Numbers

   Computer clocks, or similar operating system or hardware values,
   provide significantly fewer real bits of unpredictability than might
   appear from their specifications.

   Tests have been done on clocks on numerous systems and it was found
   that their behavior can vary widely and in unexpected ways.  One
   version of an operating system running on one set of hardware may
   actually provide, say, microsecond resolution in a clock while a
   different configuration of the "same" system may always provide the
   same lower bits and only count in the upper bits at much lower
   resolution.  This means that successive reads on the clock may
   produce identical values even if enough time has passed that the
   value "should" change based on the nominal clock resolution. There
   are also cases where frequently reading a clock can produce
   artificial sequential values because of extra code that checks for



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   the clock being unchanged between two reads and increases it by one!
   Designing portable application code to generate unpredictable numbers
   based on such system clocks is particularly challenging because the
   system designer does not always know the properties of the system
   clocks that the code will execute on.

   Use of a hardware serial number such as an Ethernet address 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 most of the Ethernet cards installed on Digital Equipment
   Corporation (DEC) hardware within DEC were manufactured by DEC
   itself, which significantly limits the range of built in addresses.

   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.

4.2 Timing and Content of External Events

   It is possible to measure the timing and content of mouse movement,
   key strokes, and similar user events.  This is a reasonable source of
   unguessable data with some qualifications.  On some machines, inputs
   such as key strokes are buffered.  Even though the user's inter-
   keystroke timing may have sufficient variation and unpredictability,
   there might not be an easy way to access that variation.  Another
   problem is that no standard method exists to sample timing details.
   This makes it hard to build standard software intended for
   distribution to a large range of machines based on this technique.

   The amount of mouse movement or the keys actually hit are usually
   easier to access than timings but may yield less unpredictability as
   the user may provide highly repetitive input.

   Other external events, such as network packet arrival times, can also
   be used with care.  In particular, the possibility of manipulation of
   such times by an adversary must be considered.

4.3 The Fallacy of Complex Manipulation

   One strategy which may give a misleading appearance of
   unpredictability is to take a very complex algorithm (or an excellent
   traditional pseudo-random number generator with good statistical
   properties) and calculate a cryptographic key by starting with the
   current value of a computer system clock as the seed.  An adversary
   who knew roughly when the generator was started would have a



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   relatively small number of seed values to test as they would know
   likely values of the system clock.  Large numbers of pseudo-random
   bits could be generated but the search space an adversary would need
   to check could be quite small.

   Thus very strong and/or complex manipulation of data will not help if
   the adversary can learn what the manipulation is and there is not
   enough unpredictability in the starting seed value.  Even if they can
   not learn what the manipulation is, they may be able to 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
   pseudo-random number generation algorithm will produce strong random
   numbers when there has been no theory behind or analysis of the
   algorithm.  There is a excellent example of this fallacy right near
   the beginning of chapter 3 in [KNUTH] where the author describes a
   complex algorithm.  It was intended that the machine language program
   corresponding to the algorithm would be so complicated that a person
   trying to read the code without comments wouldn't know what the
   program was doing.  Unfortunately, actual use of this algorithm
   showed that it almost immediately converged to a single repeated
   value in one case and a small cycle of values in another case.

   Not only does complex manipulation not help you if you have a limited
   range of seeds but blindly chosen complex manipulation can destroy
   the randomness in a good seed!

4.4 The Fallacy of Selection from a Large Database

   Another strategy that can give a misleading appearance of
   unpredictability is selection of a quantity randomly from a database
   and assume that its strength is related to the total number of bits
   in the database.  For example, typical USENET servers as of this date
   process over 35 megabytes of information per day.  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 more like 2 or 3 bits of
   information per byte, it doesn't yield 32*2.5 = 80 bits of
   unguessability.  For an adversary with access to the same 35
   megabytes the unguessability rests only on the starting point of the
   selection.  That is, at best, about 25 bits of unguessability in this
   case.

   The same argument applies to selecting sequences from the data on a
   CD ROM or Audio CD recording or any other large public database.  If
   the adversary has access to the same database, this "selection from a



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   large volume of data" step buys very little.  However, if a selection
   can be made from data to which the adversary has no access, such as
   system buffers on an active multi-user system, it may be of some
   help.

5. Hardware for Randomness

   Is there any hope for strong portable randomness in the future?
   There might be.  All that's needed is a physical source of
   unpredictable numbers.

   A thermal noise or radioactive decay source and a fast, free-running
   oscillator would do the trick directly [GIFFORD].  This is a trivial
   amount of hardware, and could easily be included as a standard part
   of a computer system's architecture.  Furthermore, any system with a
   spinning disk or the like has an adequate source of randomness
   [DAVIS].  All that's needed is the common perception among computer
   vendors that this small additional hardware and the software to
   access it is necessary and useful.

5.1 Volume Required

   How much unpredictability is needed?  Is it possible to quantify the
   requirement in, say, number of random bits per second?

   The answer is not very much is needed.  For DES, the key is 56 bits
   and, as we show in an example in Section 8, even the highest security
   system is unlikely to require a keying material of over 200 bits.  If
   a series of keys are needed, it can be generated from a strong random
   seed using a cryptographically strong sequence as explained in
   Section 6.3.  A few hundred random bits generated 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 high security
   applications to wait 200 seconds occasionally.

   These numbers are trivial to achieve.  It could be done by a person
   repeatedly tossing a coin.  Almost any hardware process is likely to
   be much faster.

5.2 Sensitivity to Skew

   Is there any specific requirement on the shape of the distribution of
   the random numbers?  The good news is the distribution need not be
   uniform.  All that is needed is a conservative estimate of how non-
   uniform it is to bound performance.  Two simple techniques to de-skew
   the bit stream are given below and stronger techniques are mentioned
   in Section 6.1.2 below.



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5.2.1 Using Stream Parity to De-Skew

   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 : 0.5 - e, where e is
   between 0 and 0.5 and is a measure of the "eccentricity" of the
   distribution.  Consider the distribution of the parity function of N
   bit samples.  The probabilities that the parity will be one or zero
   will be the sum of the odd or even terms in the binomial expansion of
   (p + q)^N, where p = 0.5 + e, the probability of a one, and q = 0.5 -
   e, the probability of a zero.

   These sums can be computed easily as

                         N            N
        1/2 * ( ( p + q )  + ( p - q )  )
   and
                         N            N
        1/2 * ( ( p + q )  - ( p - q )  ).

   (Which one corresponds to the probability the parity will be 1
   depends on whether N is odd or even.)

   Since p + q = 1 and p - q = 2e, these expressions reduce to

                       N
        1/2 * [1 + (2e) ]
   and
                       N
        1/2 * [1 - (2e) ].

   Neither of these will ever be exactly 0.5 unless e is zero, but we
   can bring them arbitrarily close to 0.5.  If we want the
   probabilities to be within some delta d of 0.5, i.e. then

                            N
        ( 0.5 + ( 0.5 * (2e)  ) )  <  0.5 + d.






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   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 which must be
   sampled for various degrees of skew in order to come within 0.001 of
   a 50/50 distribution.

                       +---------+--------+-------+
                       | Prob(1) |    e   |    N  |
                       +---------+--------+-------+
                       |   0.5   |  0.00  |    1  |
                       |   0.6   |  0.10  |    4  |
                       |   0.7   |  0.20  |    7  |
                       |   0.8   |  0.30  |   13  |
                       |   0.9   |  0.40  |   28  |
                       |   0.95  |  0.45  |   59  |
                       |   0.99  |  0.49  |  308  |
                       +---------+--------+-------+

   The last entry shows that even if the distribution is skewed 99% in
   favor of ones, the parity of a string of 308 samples will be within
   0.001 of a 50/50 distribution.

5.2.2 Using Transition Mappings to De-Skew

   Another technique, originally due to von Neumann [VON NEUMANN], is to
   examine a bit stream as a sequence of non-overlapping 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 0.5-e where e is the eccentricity of the source
   and described in the previous section.  Then the probability of each
   pair is as follows:

            +------+-----------------------------------------+
            | pair |            probability                  |
            +------+-----------------------------------------+
            |  00  | (0.5 - e)^2          =  0.25 - e + e^2  |
            |  01  | (0.5 - e)*(0.5 + e)  =  0.25     - e^2  |
            |  10  | (0.5 + e)*(0.5 - e)  =  0.25     - e^2  |
            |  11  | (0.5 + e)^2          =  0.25 + e + e^2  |
            +------+-----------------------------------------+

   This technique will completely eliminate any bias but at the expense
   of taking an indeterminate number of input bits for any particular
   desired number of output bits.  The probability of any particular
   pair being discarded is 0.5 + 2e^2 so the expected number of input
   bits to produce X output bits is X/(0.25 - e^2).



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   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 mis-read slightly so that overlapping successive bits
   pairs were used instead of non-overlapping pairs, the statistical
   analysis given is the same; however, instead of provided an unbiased
   uncorrelated series of random 1's and 0's, it instead produces a
   totally predictable sequence of exactly alternating 1's and 0's.

5.2.3 Using FFT to De-Skew

   When real world data consists of strongly biased or correlated bits,
   it may still contain useful amounts of randomness.  This randomness
   can be extracted through use of the discrete Fourier transform or its
   optimized variant, the FFT.

   Using the Fourier transform of the data, strong correlations can be
   discarded.  If adequate data is processed and remaining correlations
   decay, spectral lines approaching statistical independence and
   normally distributed randomness can be produced [BRILLINGER].

5.2.4 Using Compression to De-Skew

   Reversible compression techniques also provide a crude method of de-
   skewing a skewed bit stream.  This follows directly from the
   definition of reversible compression and the formula in Section 2
   above for the amount of information in a sequence.  Since the
   compression is reversible, the same amount of information must be
   present in the shorter output than was present in the longer input.
   By the Shannon information equation, this is only possible if, on
   average, the probabilities of the different shorter sequences are
   more uniformly distributed than were the probabilities of the longer
   sequences.  Thus the shorter sequences are de-skewed relative to the
   input.

   However, many compression techniques add a somewhat predicatable
   preface to their output stream and may insert such a sequence again
   periodically in their output or otherwise introduce subtle patterns
   of their own.  They should be considered only a rough technique
   compared with those described above or in Section 6.1.2.  At a
   minimum, the beginning of the compressed sequence should be skipped
   and only later bits used for applications requiring random bits.



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5.3 Existing Hardware Can Be Used For Randomness

   As described below, many computers come with hardware that can, with
   care, be used to generate truly random quantities.

5.3.1 Using Existing Sound/Video Input

   Increasingly computers are being built with inputs that digitize some
   real world analog source, such as sound from a microphone or video
   input from a camera.  Under appropriate circumstances, such input can
   provide reasonably high quality random bits.  The "input" from a
   sound digitizer with no source plugged in or a camera with the lens
   cap on, if the system has enough gain to detect anything, is
   essentially thermal noise.

   For example, on a SPARCstation, one can read from the /dev/audio
   device with nothing plugged into the microphone jack.  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 de-skewed as described elsewhere.

   Combining this with compression to de-skew one can, in UNIXese,
   generate a huge amount of medium quality random data by doing

        cat /dev/audio | compress - >random-bits-file

5.3.2 Using Existing Disk Drives

   Disk drives have small random fluctuations in their rotational speed
   due to chaotic air turbulence [DAVIS].  By adding low level disk seek
   time instrumentation to a system, a series of measurements can be
   obtained that include this randomness. Such data is usually highly
   correlated so that significant processing is needed, including FFT
   (see section 5.2.3).  Nevertheless experimentation has shown that,
   with such processing, disk drives easily produce 100 bits a minute or
   more of excellent random data.

   Partly offsetting this need for processing is the fact that disk
   drive failure will normally be rapidly noticed.  Thus, problems with
   this method of random number generation due to hardware failure are
   very unlikely.

6. Recommended Non-Hardware Strategy

   What is the best overall strategy for meeting the requirement for
   unguessable random numbers in the absence of a reliable hardware
   source?  It is to obtain random input from a large number of
   uncorrelated sources and to mix them with a strong mixing function.



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   Such a function will preserve the randomness present in any of the
   sources even if other quantities being combined are fixed or easily
   guessable.  This may be advisable even with a good hardware source as
   hardware can also fail, though this should be weighed against any
   increase in the chance of overall failure due to added software
   complexity.

6.1 Mixing Functions

   A strong mixing function is one which combines two or more inputs and
   produces an output where each output bit is a different complex non-
   linear function of all the input bits.  On average, changing any
   input bit will change about half the output bits.  But because the
   relationship is complex and non-linear, 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 to a shorter stream which is more random, as discussed
   in Section 5.2 above.  This is simply another case where a strong
   mixing function is desired, mixing the input bits to produce a
   smaller number of output bits.  The technique given in Section 5.2.1
   of using the parity of a number of bits is simply the result of
   successively Exclusive Or'ing them which is examined as a trivial
   mixing function immediately below.  Use of stronger mixing functions
   to extract more of the randomness in a stream of skewed bits is
   examined in Section 6.1.2.

6.1.1 A Trivial Mixing Function

   A trivial example for single bit inputs is the Exclusive Or function,
   which is equivalent to addition without carry, as show in the table
   below.  This is a degenerate case in which the one output bit always
   changes for a change in either input bit.  But, despite its
   simplicity, it will still provide a useful illustration.

                   +-----------+-----------+----------+
                   |  input 1  |  input 2  |  output  |
                   +-----------+-----------+----------+
                   |     0     |     0     |     0    |
                   |     0     |     1     |     1    |
                   |     1     |     0     |     1    |
                   |     1     |     1     |     0    |
                   +-----------+-----------+----------+

   If inputs 1 and 2 are uncorrelated and combined in this fashion then
   the output will be an even better (less skewed) random bit than the
   inputs.  If we assume an "eccentricity" e as defined in Section 5.2
   above, then the output eccentricity relates to the input eccentricity



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   as follows:

        e       = 2 * e        * e
         output        input 1    input 2

   Since e is never greater than 1/2, the eccentricity is always
   improved except in the case where at least one input is a totally
   skewed constant.  This is illustrated in the following table where
   the top and left side values are the two input eccentricities and the
   entries are the output eccentricity:

     +--------+--------+--------+--------+--------+--------+--------+
     |    e   |  0.00  |  0.10  |  0.20  |  0.30  |  0.40  |  0.50  |
     +--------+--------+--------+--------+--------+--------+--------+
     |  0.00  |  0.00  |  0.00  |  0.00  |  0.00  |  0.00  |  0.00  |
     |  0.10  |  0.00  |  0.02  |  0.04  |  0.06  |  0.08  |  0.10  |
     |  0.20  |  0.00  |  0.04  |  0.08  |  0.12  |  0.16  |  0.20  |
     |  0.30  |  0.00  |  0.06  |  0.12  |  0.18  |  0.24  |  0.30  |
     |  0.40  |  0.00  |  0.08  |  0.16  |  0.24  |  0.32  |  0.40  |
     |  0.50  |  0.00  |  0.10  |  0.20  |  0.30  |  0.40  |  0.50  |
     +--------+--------+--------+--------+--------+--------+--------+

   However, keep in mind that the above calculations assume that the
   inputs are not correlated.  If the inputs were, say, the parity of
   the number of minutes from midnight on two clocks accurate to a few
   seconds, then each might appear random if sampled at random intervals
   much longer than a minute.  Yet if they were both sampled and
   combined with xor, the result would be zero most of the time.

6.1.2 Stronger Mixing Functions

   The US Government Data Encryption Standard [DES] is an example of a
   strong mixing function for multiple bit quantities.  It takes up to
   120 bits of input (64 bits of "data" and 56 bits of "key") and
   produces 64 bits of output each of which is dependent on a complex
   non-linear function of all input bits.  Other strong 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 the "message digest" or
   hashing functions such as The US Government Secure Hash Standard
   [SHS] and the MD2, MD4, MD5 [MD2, MD4, MD5] series.  These functions
   all take an arbitrary amount of input and produce an output mixing
   all the input bits. The MD* series produce 128 bits of output and SHS
   produces 160 bits.






Eastlake, Crocker & Schiller                                   [Page 16]
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   Although the message digest functions are designed for variable
   amounts of input, DES and other encryption functions can also be used
   to combine any number of inputs.  If 64 bits of output is adequate,
   the inputs can be packed into a 64 bit data quantity and successive
   56 bit keys, padding with zeros if needed, which are then used to
   successively encrypt using DES in Electronic Codebook Mode [DES
   MODES].  If more than 64 bits of output are needed, use more complex
   mixing.  For example, if inputs are packed into three quantities, A,
   B, and C, use DES 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 to produce multiple outputs.  But keep in mind that it is
   impossible to get more bits of "randomness" out than are put in.

   An example of using a strong mixing function would be to reconsider
   the case of a string of 308 bits each of which is biased 99% towards
   zero.  The parity technique given in Section 5.2.1 above reduced this
   to one bit with only a 1/1000 deviance from being equally likely a
   zero or one.  But, applying the equation for information given in
   Section 2, this 308 bit sequence has 5 bits of information in it.
   Thus hashing it with SHS or MD5 and taking the bottom 5 bits of the
   result would yield 5 unbiased random bits as opposed to the single
   bit given by calculating the parity of the string.

6.1.3 Diffie-Hellman as a Mixing Function

   Diffie-Hellman exponential key exchange is a technique that yields a
   shared secret between two parties that can be made computationally
   infeasible for a third party to determine even if they can observe
   all the messages between the two communicating parties.  This shared
   secret is a mixture of initial quantities generated by each of them
   [D-H].  If these initial quantities are random, then the shared
   secret contains the combined randomness of them both, assuming they
   are uncorrelated.

6.1.4 Using a Mixing Function to Stretch Random Bits

   While it is not necessary for a mixing function to produce the same
   or fewer bits than its inputs, mixing bits cannot "stretch" the
   amount of random unpredictability present in the inputs.  Thus four
   inputs of 32 bits each where there is 12 bits worth of
   unpredicatability (such as 4,096 equally probable values) in each
   input cannot produce more than 48 bits worth of unpredictable output.
   The output can be expanded to hundreds or thousands of bits by, for
   example, mixing with successive integers, but the clever adversary's



Eastlake, Crocker & Schiller                                   [Page 17]
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   search space is still 2^48 possibilities.  Furthermore, mixing to
   fewer bits than are input will tend to strengthen the randomness of
   the output the way using Exclusive Or to produce one bit from two did
   above.

   The last table in Section 6.1.1 shows that mixing a random bit with a
   constant bit with Exclusive Or will produce a random bit.  While this
   is true, it does not provide a way to "stretch" one random bit into
   more than one.  If, for example, a random bit is mixed with a 0 and
   then with a 1, this produces a two bit sequence but it will always be
   either 01 or 10.  Since there are only two possible values, there is
   still only the one bit of original randomness.

6.1.5 Other Factors in Choosing a Mixing Function

   For local use, DES has the advantages that it has been widely tested
   for flaws, is widely documented, and is widely implemented with
   hardware and software implementations available all over the world
   including source code available by anonymous FTP.  The SHS and MD*
   family are younger algorithms which have been less tested but there
   is no particular reason to believe they are flawed.  Both MD5 and SHS
   were derived from the earlier MD4 algorithm.  They all have source
   code available by anonymous FTP [SHS, MD2, MD4, MD5].

   DES and SHS have been vouched for the the US National Security Agency
   (NSA) on the basis of criteria that primarily remain secret.  While
   this is the cause of much speculation and doubt, investigation of DES
   over the years has indicated that NSA involvement in modifications to
   its design, which originated with IBM, was primarily to strengthen
   it.  No concealed or special weakness has been found in DES.  It is
   almost certain that the NSA modification to MD4 to produce the SHS
   similarly strengthened the algorithm, possibly against threats not
   yet known in the public cryptographic community.

   DES, SHS, MD4, and MD5 are royalty free for all purposes.  MD2 has
   been freely licensed only for non-profit use in connection with
   Privacy Enhanced Mail [PEM].  Between the MD* algorithms, some people
   believe that, as with "Goldilocks and the Three Bears", MD2 is strong
   but too slow, MD4 is fast but too weak, and MD5 is just right.

   Another advantage of the MD* or similar hashing algorithms over
   encryption algorithms is that they are not subject to the same
   regulations imposed by the US Government prohibiting the unlicensed
   export or import of encryption/decryption software and hardware.  The
   same should be true of DES rigged to produce an irreversible hash
   code but most DES packages are oriented to reversible encryption.





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6.2 Non-Hardware Sources of Randomness

   The best source of input for mixing would be a hardware randomness
   such as disk drive timing affected by air turbulence, audio input
   with thermal noise, or radioactive decay.  However, if that is not
   available there are other possibilities.  These include system
   clocks, system or input/output buffers, user/system/hardware/network
   serial numbers and/or addresses and timing, and user input.
   Unfortunately, any of these sources can produce limited or
   predicatable values under some circumstances.

   Some of the sources listed above would be quite strong on multi-user
   systems where, in essence, each user of the system is a source of
   randomness.  However, on a small single user system, such as a
   typical IBM PC or Apple Macintosh, 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.  For
   example, 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 a few bits of randomness if the
   inter-keystroke interval is unique in the sequence up to that point
   and a similar assumption if the key hit is unique but assuming that
   no bits of randomness are present in the initial key value or if the
   timing or key value duplicate previous values.  The results of mixing
   these timings and characters typed could be further combined with
   clock values and other inputs.

   This strategy may make practical portable code to produce good random
   numbers for security even if some of the inputs are very weak on some
   of the target systems.  However, it may still fail against a high
   grade attack on small single user systems, especially if the
   adversary has ever been able to observe the generation process in the
   past.  A hardware based random source is still preferable.

6.3 Cryptographically Strong Sequences

   In cases where a series of random quantities must be generated, an
   adversary may learn some values in the sequence.  In general, they
   should not be able to predict other values from the ones that they
   know.






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   The correct technique is to start with a strong random seed, take
   cryptographically strong steps from that seed [CRYPTO2, CRYPTO3], and
   do not reveal the complete state of the generator in the sequence
   elements.  If each value in the sequence can be calculated in a fixed
   way from the previous value, then when any value is compromised, all
   future values can be determined.  This would be the case, for
   example, if each value were a constant function of the previously
   used values, even if the function were a very strong, non-invertible
   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.

6.3.1 Traditional Strong Sequences

   A traditional way to achieve a strong sequence has been to have the
   values be produced by hashing the quantities produced by
   concatenating the seed with successive integers or the like and then
   mask the values obtained so as to limit the amount of generator state
   available to the adversary.

   It may also be possible to use an "encryption" algorithm with a
   random key and seed value to encrypt and feedback some or all of the
   output encrypted value into the value to be encrypted for the next
   iteration.  Appropriate feedback techniques will usually be
   recommended with the encryption algorithm.  An example is shown below
   where shifting and masking are used to combine the cypher output
   feedback.  This type of feedback is recommended by the US Government
   in connection with DES [DES MODES].
















Eastlake, Crocker & Schiller                                   [Page 20]
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      +---------------+
      |       V       |
      |  |     n      |
      +--+------------+
            |      |           +---------+
            |      +---------> |         |      +-----+
         +--+                  | Encrypt | <--- | Key |
         |           +-------- |         |      +-----+
         |           |         +---------+
         V           V
      +------------+--+
      |      V     |  |
      |       n+1     |
      +---------------+

   Note that if a shift of one is used, this is the same as the shift
   register technique described in Section 3 above but with the all
   important difference that the feedback is determined by a complex
   non-linear 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
   will 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 "non-invertible" hashing involved with
   only partial information available.  The less information revealed
   each iteration, the harder it will be for an adversary to predict the
   sequence.  Thus it is best to use only one bit from each value.  It
   has been shown that in some cases this makes it impossible to break a
   system even when the cryptographic system is invertible and can be
   broken if all of each generated value was revealed.

6.3.2 The Blum Blum Shub Sequence Generator

   Currently the generator which has the strongest public proof of
   strength is called the Blum Blum Shub generator after its inventors
   [BBS].  It is also very simple and is based on quadratic residues.
   It's only disadvantage is that is is computationally intensive
   compared with the traditional techniques give in 6.3.1 above.  This
   is not a serious 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 intersting 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.

7. Key Generation Standards

   Several public standards are now in place for the generation of keys.
   Two of these are described below.  Both use DES but any equally
   strong or stronger mixing function could be substituted.








Eastlake, Crocker & Schiller                                   [Page 22]
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7.1 US DoD Recommendations for Password Generation

   The United States Department of Defense has specific recommendations
   for password generation [DoD].  They suggest using the US Data
   Encryption Standard [DES] in Output Feedback Mode [DES MODES] as
   follows:

        use an initialization vector determined from
             the system clock,
             system ID,
             user ID, and
             date and time;
        use a key determined from
             system interrupt registers,
             system status registers, and
             system counters; and,
        as plain text, use an external randomly generated 64 bit
        quantity such as 8 characters typed in by a system
        administrator.

   The password can then be calculated from the 64 bit "cipher text"
   generated in 64-bit Output Feedback Mode.  As many bits as are needed
   can be taken from these 64 bits and expanded into a pronounceable
   word, phrase, or other format if a human being needs to remember the
   password.

7.2 X9.17 Key Generation

   The American National Standards Institute has specified a method for
   generating a sequence of keys as follows:

        s  is the initial 64 bit seed
         0

        g  is the sequence of generated 64 bit key quantities
         n

        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








Eastlake, Crocker & Schiller                                   [Page 23]
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        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.

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.

8.1  Password Generation

   Assume that user passwords change once a year and it is desired that
   the probability that an adversary could guess the password for a
   particular account be less than one in a thousand.  Further assume
   that sending a password to the system is the only way to try a
   password.  Then the crucial question is how often an adversary can
   try possibilities.  Assume that delays have been introduced into a
   system so that, at most, an adversary can make one password try every
   six seconds.  That's 600 per hour or about 15,000 per day or about
   5,000,000 tries in a year.  Assuming any sort of monitoring, it is
   unlikely someone could actually try continuously for a year.  In
   fact, even if log files are only checked monthly, 500,000 tries is
   more plausible before the attack is noticed and steps taken to change
   passwords and make it harder to try more passwords.

   To have a one in a thousand chance of guessing the password in
   500,000 tries implies a universe of at least 500,000,000 passwords or
   about 2^29.  Thus 29 bits of randomness are needed. This can probably
   be achieved using the US DoD recommended inputs for password
   generation as it has 8 inputs which probably average over 5 bits of
   randomness each (see section 7.1).  Using a list of 1000 words, the
   password could be expressed as a three word phrase (1,000,000,000
   possibilities) or, using case insensitive letters and digits, six
   would suffice ((26+10)^6 = 2,176,782,336 possibilities).

   For a higher security password, the number of bits required goes up.
   To decrease the probability by 1,000 requires increasing the universe
   of passwords by the same factor which adds about 10 bits.  Thus to
   have only a one in a million chance of a password being guessed under
   the above scenario would require 39 bits of randomness and a password



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   that was a four word phrase from a 1000 word list or eight
   letters/digits.  To go to a one in 10^9 chance, 49 bits of randomness
   are needed implying a five word phrase or ten letter/digit password.

   In a real system, of course, there are also other factors.  For
   example, the larger and harder to remember passwords are, the more
   likely users are to write them down resulting in an additional risk
   of compromise.

8.2 A Very High Security Cryptographic Key

   Assume that a very high security key is needed for symmetric
   encryption / decryption between two parties.  Assume an adversary can
   observe communications and knows the algorithm being used.  Within
   the field of random possibilities, the adversary can try key values
   in hopes of finding the one in use.  Assume further that brute force
   trial of keys is the best the adversary can do.

8.2.1 Effort per Key Trial

   How much effort will it take to try each key?  For very high security
   applications it is best to assume a low value of effort.  Even if it
   would clearly take tens of thousands of computer cycles or more to
   try a single key, there may be some pattern that enables huge blocks
   of key values to be tested with much less effort per key.  Thus it is
   probably best to assume no more than a couple hundred cycles per key.
   (There is no clear lower bound on this as computers operate in
   parallel on a number of bits and a poor encryption algorithm could
   allow many keys or even groups of keys to be tested in parallel.
   However, we need to assume some value and can hope that a reasonably
   strong algorithm has been chosen for our hypothetical high security
   task.)

   If the adversary can command a highly parallel processor or a large
   network of work stations, 2*10^10 cycles per second is probably a
   minimum assumption for availability today.  Looking forward just a
   couple years, there should be at least an order of magnitude
   improvement.  Thus assuming 10^9 keys could be checked per second or
   3.6*10^11 per hour or 6*10^13 per week or 2.4*10^14 per month is
   reasonable.  This implies a need for a minimum of 51 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).







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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 half-encrypt the known or chosen plain
   text with all possible first half-keys, sort the output, then half-
   decrypt the encoded text with all the second half-keys.  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 large but roughly constant factor of
   effort.  To be assured of safety against this, a doubling of the
   amount of randomness in the key to a minimum of 102 bits is required.

   The meet in the middle attack assumes that the cryptographic
   algorithm can be decomposed in this way but we can not rule that out
   without a deep knowledge of the algorithm.  Even if a basic algorithm
   is not subject to a meet in the middle attack, an attempt to produce
   a stronger algorithm by applying the basic algorithm twice (or two
   different algorithms sequentially) with different keys may gain less
   added security than would be expected.  Such a composite algorithm
   would be subject to a meet in the middle attack.

   Enormous resources may be required to mount a meet in the middle
   attack but they are probably within the range of the national
   security services of a major nation.  Essentially all nations spy on
   other nations government traffic and several nations are believed to
   spy on commercial traffic for economic advantage.

8.2.3 Other Considerations

   Since we have not even considered the possibilities of special
   purpose code breaking hardware or just how much of a safety margin we
   want beyond our assumptions above, probably a good minimum for a very
   high security cryptographic key is 128 bits of randomness which
   implies a minimum key length of 128 bits.  If the two parties agree
   on a key by Diffie-Hellman exchange [D-H], then in principle only
   half of this randomness would have to be supplied by each party.
   However, there is probably some correlation between their random
   inputs so it is probably best to assume that each party needs to



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   provide at least 96 bits worth of randomness for very high security
   if Diffie-Hellman is used.

   This amount of randomness is beyond the limit of that in the inputs
   recommended by the US DoD for password generation and could require
   user typing timing, hardware random number generation, or other
   sources.

   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
   key size derived above.

9. Conclusion

   Generation of unguessable "random" secret quantities for security use
   is an essential but difficult task.

   We have shown that hardware techniques to produce such randomness
   would be relatively simple.  In particular, the volume and quality
   would not need to be high and existing computer hardware, such as
   disk drives, can be used.  Computational techniques are available to
   process low quality random quantities from multiple sources or a
   larger quantity of such low quality input from one source and produce
   a smaller quantity of higher quality, less predictable key material.
   In the absence of hardware sources of randomness, a variety of user
   and software sources can frequently be used instead with care;
   however, most modern systems already have hardware, such as disk
   drives or audio input, that could be used to produce high quality
   randomness.

   Once a sufficient quantity of high quality seed key material (a few
   hundred bits) is available, strong computational techniques are
   available to produce cryptographically strong sequences of
   unpredicatable quantities from this seed material.

10. Security Considerations

   The entirety of this document concerns techniques and recommendations
   for generating unguessable "random" quantities for use as passwords,
   cryptographic keys, and similar security uses.








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References

   [ASYMMETRIC] - Secure Communications and Asymmetric Cryptosystems,
   edited by Gustavus J. Simmons, AAAS Selected Symposium 69, Westview
   Press, Inc.

   [BBS] - A Simple Unpredictable Pseudo-Random Number Generator, SIAM
   Journal on Computing, v. 15, n. 2, 1986, L. Blum, M. Blum, & M. Shub.

   [BRILLINGER] - Time Series: Data Analysis and Theory, Holden-Day,
   1981, David Brillinger.

   [CRC] - C.R.C. Standard Mathematical Tables, Chemical Rubber
   Publishing Company.

   [CRYPTO1] - Cryptography: A Primer, A Wiley-Interscience Publication,
   John Wiley & Sons, 1981, Alan G. Konheim.

   [CRYPTO2] - Cryptography:  A New Dimension in Computer Data Security,
   A Wiley-Interscience Publication, John Wiley & Sons, 1982, Carl H.
   Meyer & Stephen M. Matyas.

   [CRYPTO3] - Applied Cryptography: Protocols, Algorithms, and Source
   Code in C, John Wiley & Sons, 1994, Bruce Schneier.

   [DAVIS] - Cryptographic Randomness from Air Turbulence in Disk
   Drives, Advances in Cryptology - Crypto '94, Springer-Verlag Lecture
   Notes in Computer Science #839, 1984, Don Davis, Ross Ihaka, and
   Philip Fenstermacher.

   [DES] -  Data Encryption Standard, United States of America,
   Department of Commerce, National Institute of Standards and
   Technology, Federal Information Processing Standard (FIPS) 46-1.
   - Data Encryption Algorithm, American National Standards Institute,
   ANSI X3.92-1981.
   (See also FIPS 112, Password Usage, which includes FORTRAN code for
   performing DES.)

   [DES MODES] - DES Modes of Operation, United States of America,
   Department of Commerce, National Institute of Standards and
   Technology, Federal Information Processing Standard (FIPS) 81.
   - Data Encryption Algorithm - Modes of Operation, American National
   Standards Institute, ANSI X3.106-1983.

   [D-H] - New Directions in Cryptography, IEEE Transactions on
   Information Technology, November, 1976, Whitfield Diffie and Martin
   E. Hellman.




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   [DoD] - Password Management Guideline, United States of America,
   Department of Defense, Computer Security Center, CSC-STD-002-85.
   (See also FIPS 112, Password Usage, which incorporates CSC-STD-002-85
   as one of its appendices.)

   [GIFFORD] - Natural Random Number, MIT/LCS/TM-371, September 1988,
   David K. Gifford

   [KNUTH] - The Art of Computer Programming, Volume 2: Seminumerical
   Algorithms, Chapter 3: Random Numbers. Addison Wesley Publishing
   Company, Second Edition 1982, Donald E. Knuth.

   [KRAWCZYK] - How to Predict Congruential Generators, Journal of
   Algorithms, V. 13, N. 4, December 1992, H. Krawczyk

   [MD2] - The MD2 Message-Digest Algorithm, RFC1319, April 1992, B.
   Kaliski
   [MD4] - The MD4 Message-Digest Algorithm, RFC1320, April 1992, R.
   Rivest
   [MD5] - The MD5 Message-Digest Algorithm, RFC1321, April 1992, R.
   Rivest

   [PEM] - RFCs 1421 through 1424:
   - RFC 1424, Privacy Enhancement for Internet Electronic Mail: Part
   IV: Key Certification and Related Services, 02/10/1993, B. Kaliski
   - RFC 1423, Privacy Enhancement for Internet Electronic Mail: Part
   III: Algorithms, Modes, and Identifiers, 02/10/1993, D. Balenson
   - RFC 1422, Privacy Enhancement for Internet Electronic Mail: Part
   II: Certificate-Based Key Management, 02/10/1993, S. Kent
   - RFC 1421, Privacy Enhancement for Internet Electronic Mail: Part I:
   Message Encryption and Authentication Procedures, 02/10/1993, J. Linn

   [SHANNON] - The Mathematical Theory of Communication, University of
   Illinois Press, 1963, Claude E. Shannon.  (originally from:  Bell
   System Technical Journal, July and October 1948)

   [SHIFT1] - Shift Register Sequences, Aegean Park Press, Revised
   Edition 1982, Solomon W. Golomb.

   [SHIFT2] - Cryptanalysis of Shift-Register Generated Stream Cypher
   Systems, Aegean Park Press, 1984, Wayne G. Barker.

   [SHS] - Secure Hash Standard, United States of American, National
   Institute of Science and Technology, Federal Information Processing
   Standard (FIPS) 180, April 1993.

   [STERN] - Secret Linear Congruential Generators are not
   Cryptograhically Secure, Proceedings of IEEE STOC, 1987, J. Stern.



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   [VON NEUMANN] - Various techniques used in connection with random
   digits, von Neumann's Collected Works, Vol. 5, Pergamon Press, 1963,
   J. von Neumann.

Authors' Addresses

   Donald E. Eastlake 3rd
   Digital Equipment Corporation
   550 King Street, LKG2-1/BB3
   Littleton, MA 01460

   Phone:   +1 508 486 6577(w)  +1 508 287 4877(h)
   EMail:   dee@lkg.dec.com


   Stephen D. Crocker
   CyberCash Inc.
   2086 Hunters Crest Way
   Vienna, VA 22181

   Phone:   +1 703-620-1222(w)  +1 703-391-2651 (fax)
   EMail:   crocker@cybercash.com


   Jeffrey I. Schiller
   Massachusetts Institute of Technology
   77 Massachusetts Avenue
   Cambridge, MA 02139

   Phone:   +1 617 253 0161(w)
   EMail:   jis@mit.edu




















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