Scholarlink

<p>In this work, we experimentally validate the effectiveness of physically implemented Asynchronous Cellular Automata (ACA) as entropy sources for True Random Number Generators (TRNG). The randomness production mechanism, initially proposed in previous research, has been rigorously tested using reconfigurable digital circuits on Field-Programmable Gate Arrays (FPGA). Random bit sequences generated at 0.8 Gbit/s successfully meet the stringent requirements for high-quality TRNG, as confirmed by statistical and entropy test suites from the US National Institute of Standards and Technology (NIST) and the German Federal Office for Information Security (BSI). Testing across multiple hardware implementations and synthesis strategies demonstrated the robustness and reliability of the ACA-based method. This paper provides a comprehensive overview of the proposed method and presents detailed experimental evidence supporting its performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Experimental validation of true randomness and entropy generation leveraging asynchronous cellular automata

  • 期刊论文

  • 发表日期：

    2025年5月27日

查看全文
  • M. L. Crespo,
  • L. De Micco,
  • M. Antonelli,
  • R. Lopresti,
  • A. Silva,
  • W. Florian Samayoa,
  • L. Garcia,
  • A. Cicuttin

摘要

In this work, we experimentally validate the effectiveness of physically implemented Asynchronous Cellular Automata (ACA) as entropy sources for True Random Number Generators (TRNG). The randomness production mechanism, initially proposed in previous research, has been rigorously tested using reconfigurable digital circuits on Field-Programmable Gate Arrays (FPGA). Random bit sequences generated at 0.8 Gbit/s successfully meet the stringent requirements for high-quality TRNG, as confirmed by statistical and entropy test suites from the US National Institute of Standards and Technology (NIST) and the German Federal Office for Information Security (BSI). Testing across multiple hardware implementations and synthesis strategies demonstrated the robustness and reliability of the ACA-based method. This paper provides a comprehensive overview of the proposed method and presents detailed experimental evidence supporting its performance.