ArticleJournal of the American Medical Informatics Association : JAMIA2021
Enabling realistic health data re-identification risk assessment through adversarial modeling.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Proposal of a procedure to stratify the reidentification risk of medical data: RIMEDA.BMC medical informatics and decision making · 2026Article
- How much of a face is a face: exploring reidentification potential with generative AI.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Practical and ready-to-use methodology to assess the re-identification risk in anonymized datasets.Scientific reports · 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
- A decade of research on genetic privacy: the findings of the GetPreCiSe Center at Vanderbilt University.Frontiers in genetics · 2025Review
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- Article
- Ethical Imperatives for Working With Diverse Populations in Digital Research.Journal of medical Internet research · 2023Article
- A guide to sharing open healthcare data under the General Data Protection Regulation.Scientific data · 2023Article
- People with disability and privacy in precision medicine research: what's at stake?Trends in genetics : TIG · 2023Article
- Managing re-identification risks while providing access to the All of Us research program.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Global healthcare fairness: We should be sharing more, not less, data.PLOS digital health · 2022Review
- Dynamically adjusting case reporting policy to maximize privacy and public health utility in the face of a pandemic.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Supporting COVID-19 Disparity Investigations with Dynamically Adjusting Case Reporting Policies.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveRe-identification risk methods for biomedical data often assume a worst case, in which attackers know all identifiable features (eg, age and race) about a subject. Yet, worst-case adversarial modeling can overestimate risk and induce heavy editing of shared data. The objective of this study is to introduce a framework for assessing the risk considering the attacker's resources and capabilities. MATERIALS AND
methodsWe integrate 3 established risk measures (ie, prosecutor, journalist, and marketer risks) and compute re-identification probabilities for data subjects. This probability is dependent on an attacker's capabilities (eg, ability to obtain external identified resources) and the subject's decision on whether to reveal their participation in a dataset. We illustrate the framework through case studies using data from over 1 000 000 patients from Vanderbilt University Medical Center and show how re-identification risk changes when attackers are pragmatic and use 2 known resources for attack: (1) voter registration lists and (2) social media posts.
resultsOur framework illustrates that the risk is substantially smaller in the pragmatic scenarios than in the worst case. Our experiments yield a median worst-case risk of 0.987 (where 0 is least risky and 1 is most risky); however, the median reduction in risk was 90.1% in the voter registration scenario and 100% in the social media posts scenario. Notably, these observations hold true for a wide range of adversarial capabilities.
conclusionsThis research illustrates that re-identification risk is situationally dependent and that appropriate adversarial modeling may permit biomedical data sharing on a wider scale than is currently the case.
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