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Selecting an Effective Entropy Estimator for Short Sequences of Bits and Bytes with Maximum Entropy
Lianet Contreras Rodríguez1, Evaristo José Madarro-Capó1, Carlos Miguel Legón-Pérez1
1Facultad de Matemática y Computación, Instituto de Criptografía, Universidad de la Habana, Habana 10400, Cuba.
Entropy (Basel, Switzerland)
|May 5, 2021
Summary
This study identifies the best method for estimating maximum entropy in short data samples. The chosen estimator minimizes bias and error for secure cryptographic applications.
Area of Science:
- Information Theory
- Cryptography
- Statistical Analysis
Background:
- Entropy quantifies uncertainty in information sources based on symbol distribution.
- Maximum Shannon's entropy is achieved with uniform symbol distribution, crucial for high-security cryptography.
- Accurate entropy estimation in short data samples is vital for cryptographic applications.
Purpose of the Study:
- To determine the most effective entropy estimator for short byte and bit samples exhibiting maximum entropy.
- To experimentally validate the performance of various entropy estimators.
Main Methods:
- Comparison of 18 different entropy estimators.
- Experimental evaluation based on bias and mean square error.
- Analysis of existing literature on estimator comparisons.
Main Results:
- Identified the most suitable estimator for short, maximum-entropy data samples.
- Experimental results guided the selection based on estimator accuracy and reliability.
- Discussion of comparative performance metrics for selected estimators.
Conclusions:
- The selected entropy estimator provides reliable uncertainty quantification for short data samples.
- This finding enhances security standards in cryptographic applications relying on high-entropy sources.
- Experimental validation confirms the estimator's efficacy in practical cryptographic scenarios.
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