Pseudo-Random Number Generation using Generative Adversarial Networks

Pseudo-random number generators (PRNG) are a fundamental element of many
security algorithms. We introduce a novel approach to their implementation, by
proposing the use of generative adversarial networks (GAN) to train a neural
network to behave as a PRNG. Furthermore, we showcase a number of interesting
modifications to the standard GAN architecture. The most significant is
partially concealing the output of the GAN's generator, and training the
adversary to discover a mapping from the overt part to the concealed part. The
generator therefore learns to produce values the adversary cannot predict,
rather than to approximate an explicit reference distribution. We demonstrate
that a GAN can effectively train even a small feed-forward fully connected
neural network to produce pseudo-random number sequences with good statistical
properties. At best, subjected to the NIST test suite, the trained generator
passed around 99% of test instances and 98% of overall tests, outperforming a
number of standard non-cryptographic PRNGs.

Authors

Marcello De Bernardi, MHR Khouzani, Pasquale Malacaria