Paper 2023/927

Collision Entropy Estimation in a One-Line Formula

Alessandro Gecchele, Università degli Studi di Brescia
Abstract

We address the unsolved question of how best to estimate the collision entropy, also called quadratic or second order Rényi entropy. Integer-order Rényi entropies are synthetic indices useful for the characterization of probability distributions. In recent decades, numerous studies have been conducted to arrive at their valid estimates starting from experimental data, so to derive suitable classification methods for the underlying processes, but optimal solutions have not been reached yet. Limited to the estimation of collision entropy, a one-line formula is presented here. The results of some specific Monte Carlo experiments give evidence of the validity of this estimator even for the very low densities of the data spread in high-dimensional sample spaces. The method strengths are unbiased consistency, generality and minimum computational cost.

Metadata
Available format(s)
PDF
Category
Foundations
Publication info
Preprint.
Keywords
Rényi entropiescollision entropy estimationcollision entropy rate estimation
Contact author(s)
a gecchele70 @ gmail com
History
2024-02-01: last of 2 revisions
2023-06-14: received
See all versions
Short URL
https://ia.cr/2023/927
License
Creative Commons Attribution-NonCommercial-NoDerivs
CC BY-NC-ND

BibTeX

@misc{cryptoeprint:2023/927,
      author = {Alessandro Gecchele},
      title = {Collision Entropy Estimation in a One-Line Formula},
      howpublished = {Cryptology {ePrint} Archive, Paper 2023/927},
      year = {2023},
      url = {https://eprint.iacr.org/2023/927}
}
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