FPGA Implementation of Pseudo-Random Number Generator for SRAM PUFs

LI Bing; ZHOU Cen-jun; CHEN Shuai; JI Jian-hua

doi:10.3969/j.issn.0372-2112.2017.09.008

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FPGA Implementation of Pseudo-Random Number Generator for SRAM PUFs

更新时间：2025-07-16

- FPGA Implementation of Pseudo-Random Number Generator for SRAM PUFs
- Acta Electronica Sinica Vol. 45, Issue 9, Pages: 2106-2112(2017)
- 作者机构：
  
  1. 东南大学微电子学院,江苏,南京,210000
  2. 深圳大学信息工程学院,广东,深圳,518060
  3. 东南大学微电子学院,江苏,南京,210000
  4. 深圳大学信息工程学院,广东,深圳,518060
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61571116)
- DOI：10.3969/j.issn.0372-2112.2017.09.008
  CLC： TP331
- Published Online：25 September 2017，
  
  Published：2017
- 稿件说明：
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LI Bing, ZHOU Cen-jun, CHEN Shuai, et al. FPGA Implementation of Pseudo-Random Number Generator for SRAM PUFs[J]. Acta Electronica Sinica, 2017, 45(9): 2106-2112.
DOI：

LI Bing, ZHOU Cen-jun, CHEN Shuai, et al. FPGA Implementation of Pseudo-Random Number Generator for SRAM PUFs[J]. Acta Electronica Sinica, 2017, 45(9): 2106-2112. DOI： 10.3969/j.issn.0372-2112.2017.09.008.

摘要

信息安全问题日益突出，而随机数则是信息安全系统的基石.本文以哈希算法为核心设计了一种伪随机数发生器，其以静态随机存储器物理不可克隆函数（Static Random Access Memory Physical Unclonable Functions，SRAM PUFs）为熵源，能够产生大量的伪随机序列.通过对熵源有效性的在线监测以及对种子的动态重播操作，本文提出的用于SRAM PUFs的伪随机数发生器提高了伪随机序列的安全性，可应用于各种高安全等级加密系统中.该发生器在FPGA开发平台上得到实现，其发生速度达598.1Mbps.随机数检测套件NIST分析结果表明：该伪随机数发生器的输出通过了所有测试项目，具有良好的随机性.

Abstract

The problem of information security is becoming serious

and the random numbers are the cornerstone of information security systems.This paper proposes a Hash-based pseudo-random number generator (PRNG) which takes static random access memory physical unclonable functions (SRAM PUFs) as entropy sources.This PRNG verifies the availability of entropy sources online and reseeds dynamically which improved the security of pseudo-random numbers.Therefore

it can be securely applied in high-level secure cryptographic protocols.This PRNG is implemented on FPGA development platform and the generation speed is up to 598.1Mbps.Experimental results of the NIST statistical test suite show that

the pseudo-random numbers generated by this PRNG pass all random tests and have good randomness.

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