ArticleJournal of the American Medical Informatics Association : JAMIA
Protecting privacy using k-anonymity.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 2 of them syntheses that pooled it.
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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
80 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Privacy technology to support data sharing for comparative effectiveness research: a systematic review.Medical care · 2013Pooled it
- High-Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million-Patient Diabetes Cohort.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Depression, anxiety, and post-traumatic stress disorder in pregnancies among Métis people in Alberta: a population-based cross-sectional study.BMC global and public health · 2026Article
- Development and validation of random-forest based federated ensemble learning algorithms for delirium prediction using electronic medical records from eleven hospitals in Austria: a retrospective study.BMC medical informatics and decision making · 2026Article
- Collaborative framework on responsible AI in LLM-driven CDSS for precision oncology leveraging real-world patient data.NPJ precision oncology · 2025Article
- Synthetic data, synthetic trust: navigating data challenges in the digital revolution.The Lancet. Digital health · 2025Review
- Large language models in clinical trials: applications, technical advances, and future directions.BMC medicine · 2025Review
- Fidelity-agnostic synthetic data generation improves utility while retaining privacy.Patterns (New York, N.Y.) · 2025Article
- Practical and ready-to-use methodology to assess the re-identification risk in anonymized datasets.Scientific reports · 2025Article
- Large language models are less effective at clinical prediction tasks than locally trained machine learning models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Parallel privacy preservation through partitioning (P4): a scalable data anonymization algorithm for health data.BMC medical informatics and decision making · 2025Article
- Mini-review of clinical data service platforms in the era of artificial intelligence: A case study of the iHi data platform.BioMedicine · 2025Review
- Article
- Employing discrete global grid systems for reproducible data obfuscation.Scientific data · 2024Article
- Financial assessment and diagnosis of abnormal data of listed companies in public health and wellness industry.Frontiers in public health · 2024Article
- Enabling Health Data Sharing with Fine-Grained Privacy.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2023Article
- A standardised differential privacy framework for epidemiological modeling with mobile phone data.PLOS digital health · 2023Article
- Reviewing Federated Machine Learning and Its Use in Diseases Prediction.Sensors (Basel, Switzerland) · 2023Review
20 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThere is increasing pressure to share health information and even make it publicly available. However, such disclosures of personal health information raise serious privacy concerns. To alleviate such concerns, it is possible to anonymize the data before disclosure. One popular anonymization approach is k-anonymity. There have been no evaluations of the actual re-identification probability of k-anonymized data sets.
designThrough a simulation, we evaluated the re-identification risk of k-anonymization and three different improvements on three large data sets. MEASUREMENT: Re-identification probability is measured under two different re-identification scenarios. Information loss is measured by the commonly used discernability metric.
resultsFor one of the re-identification scenarios, k-Anonymity consistently over-anonymizes data sets, with this over-anonymization being most pronounced with small sampling fractions. Over-anonymization results in excessive distortions to the data (i.e., high information loss), making the data less useful for subsequent analysis. We found that a hypothesis testing approach provided the best control over re-identification risk and reduces the extent of information loss compared to baseline k-anonymity.
conclusionGuidelines are provided on when to use the hypothesis testing approach instead of baseline k-anonymity.
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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.