一种基于熵源分离模型的可重构安全原语

在信息爆炸时代，信息的安全问题受到了广泛关注。在物联网设备的加密协议中，物理不可克隆函数(PUF)与真随机数发生器成为加密协议中基本的安全原语，提供了轻量级的解决方案。文章提出了一种熵源分离模型，能够分离环形振荡器中抖动(真随机数发生器的熵)和工艺偏差(PUF熵)引起的延时。基于该模型，在FPGA上设计了一种可重构的双工作模式电路，通过改变模式可分别生成PUF和真随机数。相较于FPGA上独立设计的PUF和真随机数发生器，该结构具有资源开销小、面积利用率高、功耗低等优势。实验结果表明，生成的PUF稳定性高、唯一性强、均匀性好；真随机数序列均通过了NIST测试，具有高随机性和不可预测性。

A Reconfigurable Security Primitive Based on Entropy SourceSeparation Model

In the era of information explosion, the issues of information security are of broad concern. The physical unclonable function (PUF) and the true random number generator become the basic security primitives, providing a lightweight solution. An entropy source separation model is proposed in this paper, which can separate the delays caused by jitter (the entropy of the true random number generator) and process deviation (PUF entropy) in a ring oscillator. A reconfigurable dual-mode circuit on FPGA was designed, which could reconfigure the PUF circuit into a true random number generator circuit by changing the mode. The proposed structure has the advantages of small resource overhead, large area utilization and low power consumption. The experimental results show that the PUF generated by the proposed structure has excellent performance in stability, uniqueness, and uniformity. The true random numbers generated by the proposed structure have high randomness and unpredictability. In addition, all true random numbers have passed the NIST test.