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dapper: Data Augmentation for Private Posterior Estimation in R

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Publicado no:arXiv.org (Dec 19, 2024), p. n/a
Autor principal: Eng, Kevin
Outros Autores: Awan, Jordan A, Nianqiao, Phyllis Ju, Rao, Vinayak A, Gong, Ruobin
Publicado em:
Cornell University Library, arXiv.org
Assuntos:
Data augmentation
Markov chains
Parameter estimation
Bayesian analysis
Statistical analysis
Statistical models
Statistical inference
Monte Carlo simulation
Privacy
Acesso em linha:Citation/Abstract
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Resumo: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
Fonte:Engineering Database