Noise-Aware Statistical Inference with Differentially Private Synthetic Data

Ossi Räisä, Joonas Jälkö, Samuel Kaski, Antti Honkela

Keywords: Differential Privacy, Synthetic Data, Statistical Inference, Multiple Imputation

TL;DR: We develop a DP synthetic data generation and analysis pipeline allowing synthetic data users to obtain valid uncertainty estimates for their downstream statistical analyses.

Abstract: Existing work has shown that analysing differentially private (DP) synthetic data as if it were real does not produce valid uncertainty estimates. We tackle this problem by combining synthetic data analysis techniques from the field of multiple imputation (MI), and synthetic data generation using a novel noise-aware (NA) synthetic data generation algorithm NAPSU-MQ into a pipeline NA+MI that allows computing accurate uncertainty estimates for population-level quantities from DP synthetic data. Our experiments demonstrate that the pipeline is able to produce accurate confidence intervals from DP synthetic data.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/noise-aware-statistical-inference-with/code)