ArticlePatterns (New York, N.Y.)2025
A consensus privacy metrics framework for synthetic data.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Next-generation synthetic trials in hematology with generative artificial intelligence.Leukemia · 2026Review
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- PRISM-G: an interpretable privacy scoring framework for assessing risk in synthetic human genome data.Bioinformatics (Oxford, England) · 2026Article
- Evaluating the quality of tabular synthetic data in health care.PLOS digital health · 2026Article
- Artificial intelligence-generated synthetic data for cancer research and clinical trials.Nature reviews. Cancer · 2026Review
- A case study comparing anonymized and synthetic health insurance claims data for medication safety assessments.NPJ digital medicine · 2026Article
- Aim high, stay private: differentially private synthetic data enables public release of behavioral health information with high utility.JAMIA open · 2026Article
- Potential of Image2Image translation in reducing AI bias attributed to differences in CT reconstruction methods: proof-of-concept study on a paired dataset.Frontiers in radiology · 2026Article
- Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Protecting patient privacy in tabular synthetic health data: a regulatory perspective.NPJ digital medicine · 2025Review
- A consensus privacy metrics framework for synthetic data.Patterns (New York, N.Y.) · 2025Article
- An assessment of synthetic data generation, use and disclosure under Canadian privacy regulations.AI and ethics · 2025Article
- On the use of synthetic data for healthcare AI in Africa: Technical performance, governance challenges, and policy readiness.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Synthetic data generation is a promising approach for sharing data for secondary purposes in sensitive sectors. However, to meet ethical standards and legislative requirements, it is necessary to demonstrate that the privacy of the individuals upon which the synthetic records are based is adequately protected. Through an expert consensus process, we developed a framework for privacy evaluation in synthetic data. The most commonly used metrics measure similarity between real and synthetic data and are assumed to capture identity disclosure. Our findings indicate that they lack precise interpretation and should be avoided. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information. The framework provides recommendations to effectively measure these types of disclosures, which also apply to differentially private synthetic data if the privacy budget is not close to zero. We further present future research opportunities to support widespread adoption of synthetic data.
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What OpenQuestion holds
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.