5 citations · 6 across the 5 of their papers we have counts for
10 papers
Statistical Properties of Nonparametric MLE under Laplace Noise
Yifei Xiong, Nianqiao Phyllis Ju, Vinayak Rao
Local differential privacy (LDP) protects individuals in a dataset by perturbing each measurement before release. For real-valued data, a widely used mechanism is additive Laplace…
Redistricting from the Bottom Up: Sampling Communities of Interest with Differential Privacy
Atticus McWhorter, Caroline Hammond, Nianqiao Phyllis Ju +1
Independent Redistricting Commissions (IRCs) are a promising tool for bottom-up redistricting, but their public testimony processes are vulnerable to adversarial manipulation. We p…
SOMA: A Novel Sampler for Bayesian Inference from Privatized Data
Yifei Xiong, Nianqiao Phyllis Ju
Making valid statistical inferences from privatized data is a key challenge in modern analysis. In Bayesian settings, data augmentation MCMC (DAMCMC) methods impute unobserved conf…
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…
Statistical Inference for Privatized Data with Unknown Sample Size
Jordan Awan, Andres Felipe Barrientos, Nianqiao Ju
We develop both theory and algorithms to analyze privatized data in unbounded differential privacy (DP), where even the sample size is considered a sensitive quantity that requires…
Spectral gap bounds for reversible hybrid Gibbs chains
Qian Qin, Nianqiao Ju, Guanyang Wang
Hybrid Gibbs samplers represent a prominent class of approximated Gibbs algorithms that utilize Markov chains to approximate conditional distributions, with the Metropolis-within-G…