In recent days I wrote some Python scripts, which generate 1024-bit prime numbers. But the next stage in my own thinking is, to try to accomplish the same thing in C++, using the GMP Multi-Precision Library, because GMP seems to be a well-supported and overall favorite C++ Multi-Precision Librrary. But when I explored this subject further, I noticed something which surprised me:

GMP is still using the ‘Linear Congruent algorithm’, as its main source of strong, pseudo-random numbers. The reason this fact surprises me is the fact that the Linear Congruent algorithm was invented as early as in the 1970s, as a cheap way to achieve pseudo-randomness, that would be good enough for games to surprise players, but which was never meant to provide crypto-quality random numbers. Actually, back in the 1970s, the registers on which this algorithm was used, may have been 16-bit or 32-bit registers, while today they are 256-bit registers, for which reason a careful and random-looking choice for the two constants is important. In fact, GMP defines the following functions, to initialize a ‘state_t’ object, to become a Linear Congruent RNG:

int gmp_randinit_lc_2exp_size (gmp_randstate_t state, mp_bitcnt_t

size)

void gmp_randinit_lc_2exp (gmp_randstate_t state, const_mpz_t a,

unsigned long c, mp_bitcnt_t m2exp)

For people who did not know, the generality of the algorithm is:

m2exp == 2 * size

X := aX + c mod 2^{m2exp}

The first of the two initializations above uses the ‘size’ parameter, in order to look up in a static, known table, what the ‘ideal’ values for the constants (a) and (c) are, to achieve maximum randomness. The second initialization allows the programmer to specify those constants himself, and poses no restrictions on what ‘m2exp’ will be.

One of the first approaches a cryptographic programmer might want to pursue, in order to generate a prime number eventually, is to read some random bits from the device-file ‘/dev/random’ (on a Linux computer), use the first initialization above, which will lead to an RNG, and then seed this RNG once from the system-provided random number, with which the programmer can then suggest both prime candidates and witnesses to determine whether the candidates are prime, until one prime number is ‘proven’.

But I see a potential ambition for any programmer who may want to go that route:

- Given that (a) and (c) are to be chosen from a known table, this presents a vulnerability, because a hypothetical attacker against this crypto-system may use these constants to gain knowledge about the internal state of the ‘state_t’ object, and therefore become aware of a limited number of prime numbers that can result, thereby narrowing his attack against eventual public keys, by only trying to prime-factorize or otherwise decrypt, using the narrowed set of primes.
*Even if*the constants (a) and (c) are secure in nature and not themselves hacked, the table presently only extends to a ‘size’ of 128 bits, which will actually mean that the modulus ‘m2exp’ is 2^{256}. And so, ‘the maximum amount of randomness’ – i.e., the Entropy – which even a 2048-bit public-key modulus can achieve, will be 256 bits. And this would also mean that the strength of the key-pair is*only*equivalent to a 128-bit, symmetrical AES key, regardless of how complex it is.- Some programmers might actually want to work with a modulus of 2
^{512}.

At the same time, there are reasons why the obvious solution, just to read all random bits from the device-file ‘/dev/urandom’, poses its own problems. One of the reasons is the fact that potentially, 300 (+) prime-number candidates may need to be generated, each of which will be 1024 bits long, and tested 200 (+) times, and that the quality of the randomness ‘/dev/urandom’ provides under those conditions may also be sub-optimal, because that source, too, is pseudo-random, and will only become minimally based on the physically-measured randomness which ‘/dev/random’ represents. And yet, ‘/dev/random’ will typically block if more than ~2048 bits are to be read from it.

I can think of an approach to solving this problem, which may overcome most of the hurdles…

(Updated 10/13/2018, 13h10 … )

Continue reading A Hypothetical Way, to Generate Bigger Random Numbers, using the GMP Library.