Min-Entropy Estimator Comparison

A min-entropy is defined as a conservative measure of uncertainty, which correspondsto the probability of guessing the most common output in a dataset. This thesis evaluatesthe performance of two min-entropy estimators defined in NIST Special Publication 800-90B, i.e. MultiMCW estimator and the Compression estimator. The comparison focuseson two key metrics: accuracy and execution time for estimating min-entropy values acrossdatasets with known true min-entropy. To ensure a fair comparison, both estimators weretested under identical conditions using synthetic datasets. All tools, configurations, andenvironments were consistent throughout the experiments. The motivation for this workis the increasing demand for reliable true random number generation (TRNG), especiallythe Quantum Random Number Generators (QRNGs), where accurate entropy estimationcan be helpful for ensuring the randomness quality and security.