Comparison Of Two Mean Vectors Under Differential Privacy For High-Dimensional Data

Abstract
The multivariate hypothesis testing problem is a more interesting task of the statistical inference for high-dimensional data nowadays, in which the dimension of the observation vectors is diverging and could even be larger than the sample size. However, in many applications of multivariate hypotheses problems, the data are highly sensitive and require privacy protection. Here we consider a private non-parametric projection test for the comparison of the high-dimensional multivariate mean vectors that guarantees strong differential privacy. The empirical evidence shows that the non-parametric projection test under differential privacy gives accurate inference under the null hypothesis and a higher power under the local alternative hypothesis.

Citation
Huang, C., Wang, D., Hu, Y., & Sartori, N. (2022). Comparison Of Two Mean Vectors Under Differential Privacy For High-Dimensional Data. International Conference on Statistics: Theory and Applications. https://doi.org/10.11159/icsta22.167

Publisher
Avestia Publishing

Conference/Event Name
Proceedings of the 4th International Conference on Statistics: Theory and Applications (ICSTA'22)

DOI
10.11159/icsta22.167

Additional Links
https://avestia.com/ICSTA2022_Proceedings/files/paper/ICSTA_167.pdf

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