ArticleJournal of the American Medical Informatics Association : JAMIA2020
Protecting patient privacy in survival analyses.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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
14 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
- Estimating covariate-balanced survival curve in distributed data environment using data collaboration quasi-experiment.Scientific reports · 2026Article
- An empirical assessment of differential privacy in real-world observational data: a case-control study of asthma exacerbation in UK Biobank linked with electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Correlation between ethical sensitivity and humanistic care ability among undergraduate nursing students: a cross-sectional study.BMC nursing · 2024Article
- Privacy-friendly evaluation of patient data with secure multiparty computation in a European pilot study.NPJ digital medicine · 2024Article
- OASIS portable: User-friendly offline suite for secure survival analysis.Molecules and cells · 2024Article
- Private Continuous Survival Analysis with Distributed Multi-Site Data.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2023Article
- dsSurvival 2.0: privacy enhancing survival curves for survival models in the federated DataSHIELD analysis system.BMC research notes · 2023Article
- Mitigating Membership Inference in Deep Survival Analyses with Differential Privacy.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- Sharing personal ECG time-series data privately.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Sharing Time-to-Event Data with Privacy Protection.Proceedings. IEEE International Conference on Healthcare Informatics · 2022Article
- Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption.Nature communications · 2021Article
- Differential privacy in health research: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Review
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
Abstract
objectiveSurvival analysis is the cornerstone of many healthcare applications in which the "survival" probability (eg, time free from a certain disease, time to death) of a group of patients is computed to guide clinical decisions. It is widely used in biomedical research and healthcare applications. However, frequent sharing of exact survival curves may reveal information about the individual patients, as an adversary may infer the presence of a person of interest as a participant of a study or of a particular group. Therefore, it is imperative to develop methods to protect patient privacy in survival analysis. MATERIALS AND
methodsWe develop a framework based on the formal model of differential privacy, which provides provable privacy protection against a knowledgeable adversary. We show the performance of privacy-protecting solutions for the widely used Kaplan-Meier nonparametric survival model.
resultsWe empirically evaluated the usefulness of our privacy-protecting framework and the reduced privacy risk for a popular epidemiology dataset and a synthetic dataset. Results show that our methods significantly reduce the privacy risk when compared with their nonprivate counterparts, while retaining the utility of the survival curves. DISCUSSION: The proposed framework demonstrates the feasibility of conducting privacy-protecting survival analyses. We discuss future research directions to further enhance the usefulness of our proposed solutions in biomedical research applications.
conclusionThe results suggest that our proposed privacy-protection methods provide strong privacy protections while preserving the usefulness of survival analyses.
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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.