dapper: Data Augmentation for Private Posterior Estimation in R

TL;DR

The paper introduces the R package dapper, which enables exact Bayesian inference using MCMC on privatized data, addressing the challenge of performing valid statistical analysis under differential privacy constraints.

Contribution

dapper provides a flexible, general-purpose tool for Bayesian inference on differentially private data, filling a critical gap in privacy-preserving statistical analysis.

Findings

Enables exact MCMC sampling with privatized data

Supports valid Bayesian inference under differential privacy

Enhances statistical analysis capabilities for privacy-protected data

Abstract

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.