ArticleMethods of information in medicine2022
A Privacy-Preserving Distributed Analytics Platform for Health Care Data.
Article in Methods of information in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- TrainTracks - federated learning for reproducible research on sensitive medical data.BMC medical informatics and decision making · 2026Article
- PASTA-4-PHT: a pipeline for automated security and technical audits for the personal health train.BMC medical informatics and decision making · 2026Article
- Privacy-preserving AUC computation in distributed machine learning with PHT-meDIC.PLOS digital health · 2025Article
- Bridging Data Silos in Oncology with Modular Software for Federated Analysis on Fast Healthcare Interoperability Resources: Multisite Implementation Study.Journal of medical Internet research · 2025Article
- FAItH: Federated Analytics and Integrated Differential Privacy with Clustering for Healthcare Monitoring.Scientific reports · 2025Article
- A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis.Scientific data · 2024Article
- GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics.BMC medical informatics and decision making · 2024Article
- [Foundations for the scientific use of extensive health care data in Germany-results of the Data Sharing working group of the Medical Informatics Initiative].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024Article
- Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
- Will it run?-A proof of concept for smoke testing decentralized data analytics experiments.Frontiers in medicine · 2023Article
- Privacy-by-Design Environments for Large-Scale Health Research and Federated Learning from Data.International journal of environmental research and public health · 2022Review
- Security and Privacy in Distributed Health Care Environments.Methods of information in medicine · 2022Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of health care data. However, data protection regulations prohibit data centralisation for analysis purposes because of potential privacy risks like the accidental disclosure of data to third parties. Therefore, alternative data usage policies, which comply with present privacy guidelines, are of particular interest.
objectiveWe aim to enable analyses on sensitive patient data by simultaneously complying with local data protection regulations using an approach called the Personal Health Train (PHT), which is a paradigm that utilises distributed analytics (DA) methods. The main principle of the PHT is that the analytical task is brought to the data provider and the data instances remain in their original location.
methodsIn this work, we present our implementation of the PHT paradigm, which preserves the sovereignty and autonomy of the data providers and operates with a limited number of communication channels. We further conduct a DA use case on data stored in three different and distributed data providers.
resultsWe show that our infrastructure enables the training of data models based on distributed data sources.
conclusionOur work presents the capabilities of DA infrastructures in the health care sector, which lower the regulatory obstacles of sharing patient data. We further demonstrate its ability to fuel medical science by making distributed data sets available for scientists or health care practitioners.
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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 Socratic graph.