Modelling with Sensitive Variables

Fuente: arXiv
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Autores principales: Chan, Felix, Matyas, Laszlo, Reguly, Agoston
Formato: Preprint
Publicado: 2024
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author Chan, Felix
Matyas, Laszlo
Reguly, Agoston
author_facet Chan, Felix
Matyas, Laszlo
Reguly, Agoston
contents The paper deals with models in which the dependent variable, some explanatory variables, or both represent sensitive data. We introduce a novel discretization method that preserves data privacy when working with such variables. A multiple discretization method is proposed that utilizes information from the different discretization schemes. We show convergence in distribution for the unobserved variable and derive the asymptotic properties of the OLS estimator for linear models. Monte Carlo simulation experiments presented support our theoretical findings. Finally, we contrast our method with a differential privacy method to estimate the Australian gender wage gap.
format Preprint
id arxiv_https___arxiv_org_abs_2403_15220
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Modelling with Sensitive Variables
Chan, Felix
Matyas, Laszlo
Reguly, Agoston
Econometrics
Methodology
60
The paper deals with models in which the dependent variable, some explanatory variables, or both represent sensitive data. We introduce a novel discretization method that preserves data privacy when working with such variables. A multiple discretization method is proposed that utilizes information from the different discretization schemes. We show convergence in distribution for the unobserved variable and derive the asymptotic properties of the OLS estimator for linear models. Monte Carlo simulation experiments presented support our theoretical findings. Finally, we contrast our method with a differential privacy method to estimate the Australian gender wage gap.
title Modelling with Sensitive Variables
topic Econometrics
Methodology
60
url https://arxiv.org/abs/2403.15220