Paper 2020/1140

On the Efficient Estimation of Min-Entropy

Yongjune Kim, Cyril Guyot, and Young-Sik Kim

Abstract

The min-entropy is an important metric to quantify randomness of generated random numbers in cryptographic applications; it measures the difficulty of guessing the most-likely output. One of the important min-entropy estimator is the compression estimator of NIST Special Publication (SP) 800-90B, which relies on Maurer's universal test. In this paper, we propose two kinds of min-entropy estimators to improve computational complexity and estimation accuracy by leveraging two variations of Maurer's test: Coron's test (for Shannon entropy) and Kim's test (for Renyi entropy). First, we propose a min-entropy estimator based on Coron's test which is computationally efficient than the compression estimator while maintaining the estimation accuracy. The secondly proposed estimator relies on Kim's test that computes the Renyi entropy. This proposed estimator improves estimation accuracy as well as computational complexity. We analytically characterize an interesting trade-off relation between theoretical gap of accuracy and variance of min-entropy estimates, which depends on the order of Renyi entropy. By taking into account this trade-off relation, we observe that the order of two is a proper assignment since the proposed estimator based on the collision entropy (i.e., the Renyi entropy of order two) provides the most accurate estimates. Moreover, the proposed estimator based on the collision entropy has a closed-form solution whereas both the compression estimator and the proposed estimator based on Coron's test do not have closed-from solutions. Numerical evaluations demonstrate that the first proposed estimator achieves the same accuracy as the compression estimator with much less computations. Moreover, the second estimator can even improve the accuracy as well as reduce the computational complexity.

Note: This manuscript propose efficient algorithms to estimate the min-entropy.