A Privacy-Preserving Distributed Analytics Platform for Health Care Data.

ABSTRACT:

Background

In 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.

Objective

We 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 analytic

SUBMITTER: Welten S 

PROVIDER: S-EPMC9246511 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature