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dapper: Data Augmentation for Private Posterior Estimation in R

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Publié dans:arXiv.org (Dec 19, 2024), p. n/a
Auteur principal: Eng, Kevin
Autres auteurs: Awan, Jordan A, Nianqiao, Phyllis Ju, Rao, Vinayak A, Gong, Ruobin
Publié:
Cornell University Library, arXiv.org
Sujets:
Data augmentation
Markov chains
Parameter estimation
Bayesian analysis
Statistical analysis
Statistical models
Statistical inference
Monte Carlo simulation
Privacy
Accès en ligne:Citation/Abstract
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Résumé:This paper serves as a reference and introduction to using the R package dapper. dapper encodes a sampling framework which allows exact Markov chain Monte Carlo simulation of parameters and latent variables in a statistical model given privatized data. The goal of this package is to fill an urgent need by providing applied researchers with a flexible tool to perform valid Bayesian inference on data protected by differential privacy, allowing them to properly account for the noise introduced for privacy protection in their statistical analysis. dapper offers a significant step forward in providing general-purpose statistical inference tools for privatized data.
ISSN:2331-8422
Source:Engineering Database