Index for Section 3

Alphabetical listing for R

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rand_ssl(3)
NAME
  rand_ssl - Pseudo-random number generator

SYNOPSIS
  #include <openssl/rand.h>

  int RAND_bytes(
	  unsigned char *buf, int num );

  int RAND_pseudo_bytes(
	  unsigned char *buf, int num );

  void RAND_seed(
	  const void *buf, int num );

  void RAND_add(
	  const void *buf, int num, int entropy );

  int RAND_status(
	  void );

  void RAND_screen(
	  void );

  int RAND_load_file(
	  const char *file, long max_bytes );

  int RAND_write_file(
	  const char *file );

  const char *RAND_file_name(
	  char *file, size_t num );

  int RAND_egd(
	  const char *path );

  void RAND_set_rand_method(
	  RAND_METHOD *meth );

  RAND_METHOD *RAND_get_rand_method(
	  void );

  RAND_METHOD *RAND_SSLeay(
	  void );

  void RAND_cleanup(
	  void );

DESCRIPTION
  These functions implement a cryptographically secure pseudo-random number
  generator (PRNG). It is used by other library functions, for example, to
  generate random keys. Applications can use it when they need randomness.

  A cryptographic PRNG must be seeded with unpredictable data, such as mouse
  movements or keys pressed at random by the user. This is described in
  RAND_add(3).	Its state can be saved in a seed file (see RAND_load_file(3))
  to avoid having to go through the seeding process whenever the application
  is started.

  For more information on how to obtain random data from the PRNG, see
  RAND_bytes(3).

  Internals

  The RAND_SSLeay() method implements a PRNG based on a cryptographic hash
  function.

  The following description of its design is based on the SSLeay
  documentation.  A good RNG includes the following components:

   1.  A good hashing algorithm to mix things up and to convert the RNG state
       to random numbers.

   2.  An initial source of random state.

   3.  The state should be very large.	If the RNG is used to generate 4096
       bit RSA keys, two 2048-bit random strings are required (at a minimum).
       If your RNG state only has 128 bits, you are limiting the search space
       to 128 bits, not 2048.  It should be easier to break a cipher than
       guess the RNG seed data.

   4.  Any RNG seed data should influence all subsequent random numbers
       generated.  This implies that any random seed data entered will have
       an influence on all subsequent random numbers generated.

   5.  When using data to seed the RNG state, the data should not be
       extractable from the RNG state.	We believe this should be a
       requirement because one possible source of secret semi-random data
       would be a private key or a password.  This data must not be disclosed
       by either subsequent random numbers or a core dump left by a program
       crash.

   6.  Given the same initial state, two systems should deviate in their RNG
       state (and hence the random numbers generated) over time if at all
       possible.

   7.  Given the random number output stream, it should not be possible to
       determine the RNG state or the next random number.

  The algorithm is as follows.

  There is global state made up of a 1023 byte buffer (the state), a working
  hash value (md), and a counter (count).

  Whenever seed data is added, it is inserted into the state as follows:

  The input is divided into blocks of 20 bytes (or less for the last block).
  Each block is run through the hash function as follows:  The data passed to
  the hash function is the current md, the same number of bytes from the
  state (the location determined by an incremented looping index) as the
  current block, the new key data block, and count (which is incremented
  after each use). The result of this is kept in md and also xored into the
  state at the same locations that were used as input into the hash function.
  This system addresses points 1 (hash function; currently SHA-1), 3 (the
  state), 4 (via the md), and 5 (by the use of a hash function and xor).

  When bytes are extracted from the RNG, the following process is used.	 For
  each group of 10 bytes (or less), you do the following:

  Input into the hash function the local md (which is initialized from the
  global md before any bytes are generated), the bytes that are to be
  overwritten by the random bytes, and bytes from the state (incrementing
  looping index).  From this digest output (which is kept in md), the top (up
  to) 10 bytes are returned to the caller and the bottom 10 bytes are xored
  into the state.

  Finally, after you finish num random bytes for the caller, count (which is
  incremented) and the local and global md are fed into the hash function and
  the results are kept in the global md.

  I believe the above addressed points 1 (use of SHA-1), 6 (by hashing into
  the 'state' the 'old' data from the caller that is about to be overwritten)
  and 7 (by not using the 10 bytes given to the caller to update the 'state',
  but they are used to update 'md').

  Of the points raised, only the second is not addressed (see RAND_add(3)).

SEE ALSO
  Functions: BN_rand(3), RAND_add(3), RAND_load_file(3), RAND_egd(3),
  RAND_bytes(3), RAND_set_rand_method(3), RAND_cleanup(3) rand(3)

Index for Section 3

Alphabetical listing for R

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