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Neural Network Based Min-entropy Estimation for Random Number Generators

  • Jing Yang
  • Shuangyi Zhu
  • Tianyu ChenEmail author
  • Yuan Ma
  • Na Lv
  • Jingqiang Lin
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 255)

Abstract

Random Number Generators (RNGs) are essential for cryptographic systems and communication security. A cryptographic application is prone to have a serious security risk if the entropy source that generates the random number cannot provide sufficient randomness (unpredictability) as expected. The min-entropy is usually employed to evaluate the unpredictability, which measures the difficulty of guessing the most likely output of RNGs. Recently, predictors for min-entropy estimation are proposed in the NIST 800-90B (90B), which attempt to predict the next sample in a sequence based on all previous samples. However, these predictors have shortfalls in evaluating random number with long dependence and multivariate due to huge time complexity (i.e., high-order polynomial time complexity). From the concept of predictors, we provide several suitable and efficient predictors based on neural networks for min-entropy estimation. The neural networks apply to approximating the Probability Distribution Function (PDF) and have a linear complexity of the sample space. Compared to the 90B’s predictors, the experimental results on various simulated source demonstrate that our proposed predictors have a comparable accuracy, and the execution efficiency has a significant improvement. Furthermore, when the sample space is over \(2^2\) and sample size is over \(10^8\), the 90B’s predictors cannot give the estimated result. Instead, our proposed predictors still can provide an accurate result.

Keywords

Random number Neural network Entropy estimation Predictor 

Notes

Acknowledgments

This work was partially supported by National Natural Science Foundation of China (No. 61602476 and No. 61772518), and Cryptography Development Foundation of China (No. MMJJ20170205).

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Jing Yang
    • 1
    • 2
    • 3
  • Shuangyi Zhu
    • 1
    • 2
    • 3
  • Tianyu Chen
    • 1
    • 2
    Email author
  • Yuan Ma
    • 1
    • 2
  • Na Lv
    • 1
    • 2
    • 3
  • Jingqiang Lin
    • 1
    • 2
  1. 1.Data Assurance and Communication Security Research CenterChinese Academy of SciencesBeijingChina
  2. 2.State Key Laboratory of Information Security, Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  3. 3.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina

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