3 papers
stat.CO2026
Particle Filter for Bayesian Inference on Privatized Data
Yu-Wei Chen, Pranav Sanghi, Jordan Awan
Differential Privacy (DP) is a probabilistic framework that protects privacy while preserving data utility. To protect the privacy of the individuals in the dataset, DP requires ad…
stat.ME2026
Optimal Debiased Inference on Privatized Data via Indirect Estimation and Parametric Bootstrap
Zhanyu Wang, Arin Chang, Jordan Awan
We design a debiased parametric bootstrap framework for statistical inference from differentially private data. Existing usage of the parametric bootstrap on privatized data ignore…
math.ST2026
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…