ArticleJournal of the American Medical Informatics Association : JAMIA
A globally optimal k-anonymity method for the de-identification of health data.
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 54 papers, 3 of them syntheses that pooled it.
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Who cites it
54 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Multiple modes of data sharing can facilitate secondary use of sensitive health data for research.BMJ global health · 2023Pooled it
- Algorithms to anonymize structured medical and healthcare data: A systematic review.Frontiers in bioinformatics · 2022Pooled it
- Privacy technology to support data sharing for comparative effectiveness research: a systematic review.Medical care · 2013Pooled it
- Ten quick tips for protecting health data using de-identification and perturbation of structured datasets.PLoS computational biology · 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
- Data linkage multiplies research insights across diverse healthcare sectors.Communications medicine · 2025Review
- The Costs of Anonymization: Case Study Using Clinical Data.Journal of medical Internet research · 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
- RespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle.iScience · 2023Article
- A data flow process for confidential data and its application in a health research project.PloS one · 2022Article
- Article
- A privacy-preserving design for sharing demand-driven patient datasets over permissioned blockchains and P2P secure transfer.PeerJ. Computer science · 2021Article
- Federated Learning on Clinical Benchmark Data: Performance Assessment.Journal of medical Internet research · 2020Article
- Evaluation of Privacy Risks of Patients' Data in China: Case Study.JMIR medical informatics · 2020Article
- Ensuring electronic medical record simulation through better training, modeling, and evaluation.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review.Journal of medical Internet research · 2019Article
- The CAMH Neuroinformatics Platform: A Hospital-Focused Brain-CODE Implementation.Frontiers in neuroinformatics · 2018Article
- Geolocation with respect to personal privacy for the Allergy Diary app - a MASK study.The World Allergy Organization journal · 2018Article
- MASK 2017: ARIA digitally-enabled, integrated, person-centred care for rhinitis and asthma multimorbidity using real-world-evidence.Clinical and translational allergy · 2018Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExplicit patient consent requirements in privacy laws can have a negative impact on health research, leading to selection bias and reduced recruitment. Often legislative requirements to obtain consent are waived if the information collected or disclosed is de-identified.
objectiveThe authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets.
designAuthors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits. Measurement Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated.
resultsThe Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution.
conclusionsFor the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.
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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.