Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?

ABSTRACT:

Background

Synthetic data have been proposed as a solution for sharing anonymized versions of sensitive biomedical datasets. Ideally, synthetic data should preserve the structure and statistical properties of the original data, while protecting the privacy of the individual subjects. Differential Privacy (DP) is currently considered the gold standard approach for balancing this trade-off.

Objectives

The aim of this study is to investigate how trustworthy are group differences discovered by independent sample tests from DP-synthetic data. The evaluation is carried out in terms of the tests' Type I and Type II errors. With the former, we can quantify the tests' validity, i.e., whether the probability of false discoveries is indeed below the significance level, and the latter i

SUBMITTER: Montoya Perez I 

PROVIDER: S-EPMC11495942 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature