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stat.CO2024
dapper: Data Augmentation for Private Posterior Estimation in R
Kevin Eng, Jordan A. Awan, Nianqiao Phyllis Ju +2
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 param…
cs.CR2024
Formal Privacy Guarantees with Invariant Statistics
Young Hyun Cho, Jordan Awan
Motivated by the 2020 US Census products, this paper extends differential privacy (DP) to address the joint release of DP outputs and nonprivate statistics, referred to as invarian…
stat.ME2024
Differentially Private Covariate Balancing Causal Inference
Yuki Ohnishi, Jordan Awan
Differential privacy is the leading mathematical framework for privacy protection, providing a probabilistic guarantee that safeguards individuals' private information when publish…