ArticleJournal of medical Internet research2020
Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed.
- Beyond classical models: LLM-driven survival analysis for breast cancer prognosis using European cancer registry data.BMC medical informatics and decision making · 2026Article
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- Anonymization and visualization of health data and biomarkers.NPJ digital medicine · 2026Article
- Reward-Guided Generation Improves the Scientific Utility of Synthetic Biomedical Data.medRxiv : the preprint server for health sciences · 2026Article
- Synthetic health data in Canada: A scoping review of methods, applications, and data sources.Canadian journal of public health = Revue canadienne de sante publique · 2026Article
- A consensus privacy metrics framework for synthetic data.Patterns (New York, N.Y.) · 2025Article
- Can Synthetic Data Allow for Smaller Sample Sizes in Chronic Urticaria Research?Clinical and translational allergy · 2025Article
- Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study.Journal of medical Internet research · 2025Article
- GenAI synthetic data create ethical challenges for scientists. Here's how to address them.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Preserving information while respecting privacy through an information theoretic framework for synthetic health data generation.NPJ digital medicine · 2025Article
- Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings?Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Large language models generating synthetic clinical datasets: a feasibility and comparative analysis with real-world perioperative data.Frontiers in artificial intelligence · 2025Article
- An assessment of synthetic data generation, use and disclosure under Canadian privacy regulations.AI and ethics · 2025Article
- Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer.JCO clinical cancer informatics · 2024Article
- De-identification is not enough: a comparison between de-identified and synthetic clinical notes.Scientific reports · 2024Article
- Synthesis and quality assessment of combined time-series and static medical data using a real-world time-series generative adversarial network.Scientific reports · 2024Article
- Generating Synthetic Electronic Health Record Data Using Generative Adversarial Networks: Tutorial.JMIR AI · 2024Article
- Article
- Evaluating the Utility and Privacy of Synthetic Breast Cancer Clinical Trial Data Sets.JCO clinical cancer informatics · 2023Article
- Implementation of Open Science Practices in Communication Sciences and Disorders Research With Black, Indigenous, and People of Color.Journal of speech, language, and hearing research : JSLHR · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere has been growing interest in data synthesis for enabling the sharing of data for secondary analysis; however, there is a need for a comprehensive privacy risk model for fully synthetic data: If the generative models have been overfit, then it is possible to identify individuals from synthetic data and learn something new about them.
objectiveThe purpose of this study is to develop and apply a methodology for evaluating the identity disclosure risks of fully synthetic data.
methodsA full risk model is presented, which evaluates both identity disclosure and the ability of an adversary to learn something new if there is a match between a synthetic record and a real person. We term this "meaningful identity disclosure risk." The model is applied on samples from the Washington State Hospital discharge database (2007) and the Canadian COVID-19 cases database. Both of these datasets were synthesized using a sequential decision tree process commonly used to synthesize health and social science data.
resultsThe meaningful identity disclosure risk for both of these synthesized samples was below the commonly used 0.09 risk threshold (0.0198 and 0.0086, respectively), and 4 times and 5 times lower than the risk values for the original datasets, respectively.
conclusionsWe have presented a comprehensive identity disclosure risk model for fully synthetic data. The results for this synthesis method on 2 datasets demonstrate that synthesis can reduce meaningful identity disclosure risks considerably. The risk model can be applied in the future to evaluate the privacy of fully synthetic data.
Indexed as
Identifiers
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.