ArticleBMC medical informatics and decision making2012
Estimating the re-identification risk of clinical data sets.
Article in BMC medical informatics and decision making, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Open tools for quantitative anonymization of tabular phenotype data: literature review.Briefings in bioinformatics · 2022Pooled it
- Algorithms to anonymize structured medical and healthcare data: A systematic review.Frontiers in bioinformatics · 2022Pooled it
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- High-Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million-Patient Diabetes Cohort.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Re-identification risk for common privacy preserving patient matching strategies when shared with de-identified demographics.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Practical and ready-to-use methodology to assess the re-identification risk in anonymized datasets.Scientific reports · 2025Article
- Parallel privacy preservation through partitioning (P4): a scalable data anonymization algorithm for health data.BMC medical informatics and decision making · 2025Article
- A survey on UK researchers' views regarding their experiences with the de-identification, anonymisation, release methods and re-identification risk estimation for clinical trial datasets.Clinical trials (London, England) · 2025Article
- A scaling law to model the effectiveness of identification techniques.Nature communications · 2025Article
- The utility of artificial intelligence in visualization of pediatric gastrointestinal mucosa.Frontiers in pediatrics · 2025Review
- Ethics of artificial intelligence in medicine.Singapore medical journal · 2024Review
- Validating a membership disclosure metric for synthetic health data.JAMIA open · 2022Article
- Autonomic aging - A dataset to quantify changes of cardiovascular autonomic function during healthy aging.Scientific data · 2022Article
- Selecting Privacy-Enhancing Technologies for Managing Health Data Use.Frontiers in public health · 2022Review
- Article
- Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis.BMC medical research methodology · 2021Article
- Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study.JMIR medical informatics · 2021Review
- Secondary Use of Clinical Data in Data-Gathering, Non-Interventional Research or Learning Activities: Definition, Types, and a Framework for Risk Assessment.Journal of medical Internet research · 2021Article
- Enabling realistic health data re-identification risk assessment through adversarial modeling.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDe-identification is a common way to protect patient privacy when disclosing clinical data for secondary purposes, such as research. One type of attack that de-identification protects against is linking the disclosed patient data with public and semi-public registries. Uniqueness is a commonly used measure of re-identification risk under this attack. If uniqueness can be measured accurately then the risk from this kind of attack can be managed. In practice, it is often not possible to measure uniqueness directly, therefore it must be estimated.
methodsWe evaluated the accuracy of uniqueness estimators on clinically relevant data sets. Four candidate estimators were identified because they were evaluated in the past and found to have good accuracy or because they were new and not evaluated comparatively before: the Zayatz estimator, slide negative binomial estimator, Pitman's estimator, and mu-argus. A Monte Carlo simulation was performed to evaluate the uniqueness estimators on six clinically relevant data sets. We varied the sampling fraction and the uniqueness in the population (the value being estimated). The median relative error and inter-quartile range of the uniqueness estimates was measured across 1000 runs.
resultsThere was no single estimator that performed well across all of the conditions. We developed a decision rule which selected between the Pitman, slide negative binomial and Zayatz estimators depending on the sampling fraction and the difference between estimates. This decision rule had the best consistent median relative error across multiple conditions and data sets.
conclusionThis study identified an accurate decision rule that can be used by health privacy researchers and disclosure control professionals to estimate uniqueness in clinical data sets. The decision rule provides a reliable way to measure re-identification risk.
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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.