Min-Entropy Estimation via Shannon Entropy Segmentation
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Solution Overview
Problem
Current methods for estimating the randomness of random number generators, particularly for cryptographic applications, are inefficient, inaccurate, and overly conservative, as they require computationally expensive tests and may not effectively validate the entropy sources in real-time.
Innovation Solution
A computer-implemented method that divides output data into blocks, estimates Shannon entropy, and uses an argument parameter to determine the probability of the most probable block being generated, thereby tuning the estimate of randomness, which corresponds to an estimate of min-entropy, improving the efficiency and accuracy of randomness estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional NIST 800-90B tests are used to validate entropy sources, then reliability of cryptographic security is improved, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent segments the entropy validation process into multiple independent statistical tests (frequency test, run test, collision test, etc.) that can be executed separately and in parallel. Each test examines a specific aspect of randomness, allowing the overall validation to be more efficient while maintaining comprehensive security assessment.
Solution Approach 2:
The patent changes the parameters of the statistical tests by using optimized thresholds and confidence levels that balance security requirements with computational efficiency. By adjusting these parameters, the system achieves adequate cryptographic validation without the excessive computational cost of traditional conservative approaches.
2Reliability
If conservative entropy estimation methods are applied, then security margin is improved, but accuracy of randomness measurement deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the results of statistical tests are used to adjust and refine the entropy estimation. The system continuously monitors the randomness quality and adjusts the entropy rate accordingly, providing both accurate measurement and adequate security margins through iterative refinement rather than conservative static estimates.
3Measurement precision
If comprehensive statistical tests are performed on entropy sources, then measurement precision of randomness is improved, but computational complexity increases
Solution Approach 1:
The patent divides the comprehensive statistical testing into modular, independent test components that can be selectively applied based on the specific entropy source and security requirements. This segmentation reduces the apparent complexity by allowing targeted testing rather than requiring all tests to be executed in full for every validation scenario.
4Reliability
If traditional entropy estimation algorithms are used, then reliability of security validation is improved, but processing speed decreases
Solution Approach 1:
The patent changes key parameters of the validation algorithm including the use of optimized statistical thresholds, adjusted confidence levels, and streamlined test sequences. These parameter changes enable the system to maintain reliable security validation while achieving the processing speeds necessary for real-time applications such as cryptographic key generation.
Data Source
AI summary
Disclosed herein is an apparatus for estimating randomness of a random number generator. The apparatus is configured to divide output data (302), generated by the random number generator (704), into blocks (310) of a length (L), estimate a Shannon entropy of a second sub-set (404) of the blocks (310), using a first sub-set (402) of the blocks (310) to initialize the estimating, solve an estimate function, that relates an argument parameter (θ) to the Shannon entropy estimate, to determine a value for the argument parameter (θ) that is indicative of a probability of a most probable block being generated by the random number generator (704) as a new block, and use the length (L) to tune an estimate of randomness of the random number generator (704) calculated based on the value for the argument parameter (θ).