.png){kind=icon}
Return
Generative Artificial Intelligence-Driven Secure Pseudorandom Number Generator
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2026.108492.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The rapid progress of generative artificial intelligence (GenAI), especially generative adversarial networks (GANs), has stimulated growing interest in learning-based pseudorandom number generation. However, most existing GAN-based PRNGs are evaluated primarily through empirical statistical batteries, providing limited formal assurance against computationally powerful adversaries and lacking reductions to established hardness assumptions. This paper proposes a PRNG construction that integrates a GAN-based Bernoulli noise expansion sampler into an LPN-based framework. We formalize a neural-structured LPN variant (GAN-LPN) that captures practical non-idealities of learned sampling via explicit, measurable deviation parameters, and we carry these terms through the security analysis as additive degradation in the final advantage bound. We then present a game-based reduction showing that, under the stated assumptions and deviation bounds, distinguishing the PRNG output from random can be reduced to the hardness of the corresponding underlying LPN-type problem. Experimental results show that the generated sequences pass NIST SP 800-22 and additional empirical benchmarks, including the Hamming Distance test and a practical next-bit prediction test, providing supplementary evidence on the statistical quality of the output.
Keywords:
Generative Adversarial Networks
Pseudorandom Number Generator
Learning with Errors
Security Reduction
Statistical Testing
Journal
F
IF:
0
Papers:
642
Citations:
0
Organization
Cited Papers
No cited papers available