Yoon Inkwon, Han Jong Hyeok, Park Byeong Uk, Jeon Hee-Jae
Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon, 24341, Korea.
Department of Smart Health Science and Technology, Kangwon National University, Chuncheon, 24341, Korea.
Sci Rep. 2024 Mar 29;14(1):7474. doi: 10.1038/s41598-024-58088-6.
The development of random number generators (RNGs) using speckle patterns is pivotal for secure encryption key generation, drawing from the recent statistical properties identified in speckle-based imaging. Speckle-based RNG systems generate a sequence of random numbers through the unpredictable and reproducible nature of speckle patterns, ensuring a source of randomness that is independent of algorithms. However, to guarantee their effectiveness and reliability, these systems demand a meticulous and rigorous approach. In this study, we present a blood-inspired RNG system with a microfluidics device, designed to generate random numbers at a rate of 5.5 MHz and a high-speed of 1250 fps. This process is achieved by directing a laser beam through a volumetric scattering medium to procure speckle patterns. Additionally, designed microfluidic device requires only a minimal blood sample of 5 µl to capture these speckle patterns effectively. After implementing the two-pass tuple-output von Neumann debiasing algorithm to counteract statistical biases, we utilized the randomness statistical test suite from the National Institute of Standards and Technology for validation. The generated numbers successfully passed these tests, ensuring their randomness and unpredictability. Our blood-inspired RNG, utilizing whole blood, offers a pathway for affordable, high-output applications in fields like encryption, computer security, and data protection.
利用散斑图案开发随机数生成器(RNG)对于安全加密密钥生成至关重要,这借鉴了基于散斑成像中最近发现的统计特性。基于散斑的RNG系统通过散斑图案不可预测且可重复的特性生成随机数序列,确保了独立于算法的随机源。然而,为了保证其有效性和可靠性,这些系统需要细致且严格的方法。在本研究中,我们展示了一种具有微流体装置的受血液启发的RNG系统,该系统设计用于以5.5 MHz的速率和1250 fps的高速生成随机数。这个过程是通过引导激光束穿过体积散射介质以获取散斑图案来实现的。此外,设计的微流体装置仅需要5 μl的微量血液样本就能有效捕获这些散斑图案。在实施两遍元组输出冯·诺依曼去偏算法以抵消统计偏差后,我们使用了美国国家标准与技术研究院的随机性统计测试套件进行验证。生成的数字成功通过了这些测试,确保了它们的随机性和不可预测性。我们受血液启发的RNG利用全血,为加密、计算机安全和数据保护等领域的经济高效、高产量应用提供了一条途径。