ArticleiScience2025
On the fidelity versus privacy and utility trade-off of synthetic patient data.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.Frontiers in digital health · 2026Pooled it
- Evaluating the quality of tabular synthetic data in health care.PLOS digital health · 2026Article
- High-Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million-Patient Diabetes Cohort.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A case study comparing anonymized and synthetic health insurance claims data for medication safety assessments.NPJ digital medicine · 2026Article
- Crossing borders securely: synthetic data and federated networks for privacy-preserving access to real-world data and emerging use cases.NPJ digital medicine · 2025Review
- A consensus privacy metrics framework for synthetic data.Patterns (New York, N.Y.) · 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of synthetic data is a widely discussed and promising solution for privacy-preserving medical research. Synthetic data may, however, not always rule out the risk of re-identifying characteristics of real patients and can vary greatly in terms of data fidelity and utility. We systematically evaluate the trade-offs between privacy, fidelity, and utility across five synthetic data models and three patient-level datasets. We evaluate fidelity based on statistical similarity to the real data, utility on three machine learning use cases, and privacy via membership inference, singling out, and attribute inference risks. Synthetic data without differential privacy (DP) maintained fidelity and utility without evident privacy breaches, whereas DP-enforced models significantly disrupted correlation structures. K-anonymity-based data sanitization of demographic features, while preserving fidelity, introduced notable privacy risks. Our findings emphasize the need to advance methods that effectively balance privacy, fidelity, and utility in synthetic patient data generation.
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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.