Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (5): 322-329.doi: 10.11896/jsjkx.240700006

• Information Security • Previous Articles     Next Articles

Error Analysis and Parameter Recommendations for Randomness Test Under Large Sample Conditions

SUN Yueyue1, FAN Limin2   

  1. 1 School of Mathematics and Statistics,Beijing Institute of Technology,Beijing 100081,China
    2 Trusted Computing and Information Assurance Laboratory,Institute of Software,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2024-07-01 Revised:2024-10-01 Online:2025-05-15 Published:2025-05-12
  • About author:SUN Yueyue,born in 1999,postgra-duate.Her main research interests include statistical test of randomness and so on.
    FAN Limin,born in 1978,Ph.D,senior engineer.Her main research interests include side channel analysis and protection,and password detection.
  • Supported by:
    National Cryptography Science Foundation of China(2025NCSF02057).

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Abstract: In the field of information security,randomness tests play a crucial role in ensuring the security of cryptographic systems.The stability and reliability of these tests directly impact the overall security of cryptographic systems,making error issues during the testing process a focal point for both academia and industry.Particularly when handling large-scale samples,the accumulation of errors can more readily lead to reliability issues in randomness testing.Consequently,studying methods to enhance the accuracy and reliability of randomness testing is of significant importance.The GM/T 0005-2021 standard outlines 9 tests with variable parameters designed for randomness testing of large binary data samples.This study categorizes these tests according to their characteristics and conducts a quantitative error analysis.Specifically,when the bit length of the binary sequence under test is 1×108,the parameters recommended by the GM/T 0005-2021 standard are generally reasonable.For the Maurer universal statistical test,a subsequence length of 6 results in upper bound p-value error of 0.001 492 8,demonstrating higher accuracy compared to the parameters suggested in the GM/T 0005-2021 standard.Similarly,for the linear complexity test,using smaller subsequence lengths results in smaller errors. With the increase in sample length,this study extends the analysis to parameter selection for a sample length of 1×109.It systematically examines the errors associated with different sample lengths and parameter configurations,providing refined parameter recommendations for randomness testing when the sample length reaches 1×109.

Key words: Randomness test, Large sample, Error analysis, Test parameters, GM/T 0005-2021

CLC Number: 

  • TN918.1
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