ArticleMethods of information in medicine2024
Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?
Article in Methods of information in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- A case study comparing anonymized and synthetic health insurance claims data for medication safety assessments.NPJ digital medicine · 2026Article
- Is synthetic data generation effective in maintaining clinical biomarkers? Investigating diffusion models across diverse imaging modalities.Frontiers in artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundSynthetic 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.
objectivesThe 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 indicates the tests' power in making real discoveries.
methodsWe evaluate the Mann-Whitney U test, Student's
conclusionA large portion of the evaluation results expressed dramatically inflated Type I errors, especially at levels of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.