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Complexity Reduction in Analyzing Independence between Statistical Randomness Tests Using Mutual Information
Jorge Augusto Karell-Albo1, Carlos Miguel Legón-Pérez1, Raisa Socorro-Llanes2
1Instituto de Criptografía, Facultad de Matemática y Computación, Universidad de la Habana, Habana 10400, Cuba.
Entropy (Basel, Switzerland)
|November 24, 2023
Summary
This study simplifies mutual information analysis for randomness tests, reducing computational complexity without losing correlation detection accuracy. The efficient method is recommended for analyzing statistical test batteries.
Area of Science:
- Information Theory
- Statistical Analysis
- Randomness Testing
Background:
- Mutual information effectively evaluates correlations between randomness tests.
- High computational complexity limits the application of mutual information for large test batteries.
Purpose of the Study:
- To reduce the complexity of mutual information-based methods for analyzing the independence of statistical randomness tests.
- To propose a modified method that maintains correlation detection capabilities while improving efficiency.
Main Methods:
- Theoretical estimation and experimental verification of complexity reduction.
- Modification of the mutual information significance determination step.
- Analysis of correlations within the NIST (National Institute of Standards and Technology) battery of randomness tests.
Main Results:
- A significant reduction in the computational complexity of the mutual information method was achieved.
- The proposed variant method demonstrated comparable correlation detection performance to the original method.
- The modified method's efficiency was experimentally validated.
Conclusions:
- The modified mutual information method offers a more efficient approach to analyzing statistical test independence.
- The method's effectiveness in detecting correlations remains robust.
- The proposed technique is recommended for broader application in analyzing various batteries of randomness tests.
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