Authors:
Virginia Graciano Martinez
1
;
Luís Ferreira Pires
1
;
Luiz Olavo Bonino da Silva Santos
2
;
1
;
João Luiz Rebelo Moreira
1
and
Renata Guizzardi-Silva Souza
1
Affiliations:
1
University of Twente, Enschede, The Netherlands
;
2
Leiden University Medical Center, Leiden, The Netherlands
Keyword(s):
Personal Health Train, Cloud, Staging Station, Data Station, Privacy Preservation.
Abstract:
Data analysis can be quite valuable for the progress of science in general, but more specifically in the healthcare domain, in which it can generate medical advances and improve healthcare services. The Personal Health Train (PHT) is an approach based on distributed learning that allows analytics to be brought to the (personal health) data rather than the other way around, allowing data privacy and control to be preserved, and ethical and legal concerns to be observed. Since computational resources are necessary whenever processing is expected to be done, a sandboxed environment should be available within the healthcare organization. This environment should allow the received algorithms to be executed without interfering with the organization’s regular processing. However, the IT infrastructure of a healthcare organization may not be powerful enough to perform a requested analysis task. This paper reports on our efforts to extend the PHT approach to allow data to be processed in the
cloud, augmenting the processing power of the IT infrastructure of healthcare organizations. Our solution not only fulfills the functional requirements of the PHT approach, but it also complies with privacy regulations, particularly the General Data Protection Rules (GDPR). The paper describes the design and implementation of our solution, also demonstrating its suitability with a simple and yet representative case study.
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