@article{Tamir2025, 
author = {Ran Tamir},
title = {Testing for correlation in Gaussian databases via local decision making},
year = {2025},
journal = {AIMS Mathematics},
volume = {10},
number = {4},
pages = {7721-7766},
keywords = {correlation detection, data privacy, de-anonymization, Gaussian database, hypothesis testing},
url = {https://www.sciopen.com/article/10.3934/math.2025355},
doi = {10.3934/math.2025355},
abstract = {We propose a computationally efficient statistical test for detecting correlation between two Gaussian databases. The problem is formulated as a hypothesis test: under the null hypothesis, the databases are independent; under the alternative hypothesis, they are correlated but subject to an unknown row permutation. We derive bounds on both type Ⅰ and type Ⅱ error probabilities and demonstrate that the proposed test outperforms a recently introduced method across a broad range of parameter settings. The test statistic is based on a sum of dependent indicator random variables. To effectively bound the type Ⅰ error probability, we introduce a novel graph-theoretic technique for bounding the    k-th moments of such statistics.}
}