Data trust models in sleep science F. Gauger, C. Erler, W. Stork FZI Forschungszentrum Informatik, Karlsruhe
DOI 10.5414/ATX02816
Abstrakt
In data spaces, data trust models (DTMs) serve as conceptually neutral intermediaries. Due to their transformational powers, DTMs enable markets in environments that otherwise fail to bring supply and demand into equilibrium. They facilitate the secure and privacy-compliant use of data across technical and institutional boundaries. By aligning the legal, economic, and organizational requirements of stakeholders, DTMs help achieve a Pareto-optimal state within the ecosystem of their potential participants. Focusing on data self-sovereignity of patients, the project SouveMed has established fundamental building blocks of a DTM specifically designed for use in sleep research and sleep medicine. Operating across institutional boundaries, SouveMed allows patients and study participants to grant access to their data under clearly defined, tiered, and revocable conditions. Researchers, in turn, can select these data using filter criteria prior to analyzing them in a de-identified form within a secure and trustworthy execution environment of the DTM. The project was funded by the German Federal Ministry of Education and Research (BMBF) under grant No. 16DTM115A-C.
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