通过跨平台的系统动进行原始QPP-RNG随机性:一个NIST SP 800-90B评估
Georgia Vrana1, Dafu Lou1, Randy Kuang2
1Quantropi (Canada), 1545 Carling Ave., Suite 620, Ottawa, ON, K1Z 8P9, Canada.
Scientific reports
|July 29, 2025
概括
在商品硬件上,QPP-RNG从系统动中产生高质量的随机性. 这种真正的随机数生成器 (TRNG) 为安全的加密应用提供了强大的源,特别是在物联网设备上.
科学领域:
- 计算机科学 计算机科学
- 密码学 密码学 密码学 密码学
- 硬件安全 硬件安全
背景情况:
- 高质量的随机性对于现代加密系统至关重要.
- 现有的真正随机数生成器 (TRNG) 通常依赖于专门的硬件或在密度上有局限性.
- 系统层面的紧张表现出一个尚未开发的源.
研究的目的:
- 介绍QPP-RNG,一个新的TRNG,利用系统级的动来实现加密随机性.
- 评估QPP-RNG在不同平台上的性能和统计特性.
- 为了证明在没有专门组件的商品硬件上产生强随机性的可行性.
主要方法:
- 从各种系统级 jitters (CPU管道时间,DRAM更新,缓存失败) 中收集.
- 测量随机数组排序操作的过期时间,这些操作受到微观震动的影响.
- 使用量子变量 (QPP) 架构将时间变化放大到密码学上强大的随机性.
- 使用NIST SP 800-90B,NIST SP 800-22和ENT测试套件进行严格的统计评估.
主要成果:
- 在Windows,macOS和Raspberry Pi中,QPP-RNG实现了高的IID最小率 (7.7-7.9位/字节).
- 始终通过了NIST SP 800-90B IID测试,p值明显高于0.01值.
- 在x86_64和ARM64架构中展示了显著的统计一致性.
- 在密度方面表现优于领先的商业源.
结论:
- 通过将系统噪声转化为可靠的流,QPP-RNG为嵌入式安全提供了一个新的范例.
- 它在通用设备上提供了强大的,高质量的源,适合资源有限的物联网和边缘计算.
- 这种方法消除了对专门硬件的需求,使强随机性更容易获得.
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