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RWTH Aachen
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Customisable Data Safes for a Distributed Analytics Platform

Thesis type
  • Bachelor
Status Running
Supervisor(s)
Advisor(s)

In recent years, as newer technologies have evolved around the healthcare ecosystem, more and more data have been generated.

Advanced analytics could power the data collected from numerous sources, both from healthcare institutions, or generated by individuals themselves via apps and devices, and lead to innovations in treatment and diagnosis of diseases; improve the care given to the patient; and empower citizens to participate in the decision-making process regarding their own health and well-being. However, the sensitive nature of the health data prohibits healthcare organizations from sharing the data.

 

The Personal Health Train (PHT) is a novel approach, aiming to establish a distributed data analytics infrastructure enabling the (re)use of distributed healthcare data, while data owners stay in control of their own data. The main principle of the PHT is that data remains in its original location, and analytical tasks visit data sources and execute the tasks. The PHT provides a distributed, flexible approach to use data in a network of participants, incorporating the FAIR principles. It facilitates the responsible use of sensitive and/or personal data by adopting international principles and regulations.

 

This Thesis focusses on the creation secure data storage containers - called data safes.

Usually, the analytical task executed at each institution needs access to the data pool and therefore direct access to the hospital information system (HIS). This can possibly pose security issues and represents a point of attack for malicious activities. One solution could be the separation of the PHT execution environment and the HIS. In order to provide data to the analytical task, a push mechanism for the data sets is needed, which transfers the sets from the HIS ecosystem into the PHT execution environment. The data sets should be stored in secure data containers (safes). These safes should provide an access regulation such that only analytical tasks having a specific signature can query the data. 


If you are interested in this thesis, a related topic or have additional questions, please do not hesitate to send a message to welten@dbis.rwth-aachen.de

Prerequisites

In-depth Knowledge in Containerisation Technologies
In-depth Knowledge in Data Security and Access Management

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