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 analytical task is brought to the data provider and the data instances remain in their original location. Methods In 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. Results We show that our infrastructure enables the training of data models based on distributed data sources. Conclusion Our 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.

Multi-Institutional Breast Cancer Detection Using a Secure On-Boarding Service for Distributed Analytics

“…The results are sent back and aggregated centrally. Several prominent representatives of DA have been introduced during the past years, and basically, all of them follow these abstract analysis strategies [12,[19][20][21][22].…”

“…Dependent on the analysis to be executed, these protocols could be more or less complex. In this work, we primarily focus on another approach: The so-called Personal Health Train (PHT) [12,19,22].…”

Security and Privacy in Distributed Health Care Environments

“…However, data protection regulations prohibit data centralization for analysis purposes because of potential risk such as unauthorized disclosure. Therefore, this paper 4 aims to enable analyses on sensitive patient data by simultaneously complying with local data protection regulations by using an approach called the personal health train (PHT), which is a paradigm that utilizes distributed analytics methods. 1 2 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.…”

A human-centered, health data-driven ecosystem

“…This allows the inclusion of other relevant data sources in the analysis [49]. It is providing virtually unlimited computing, scalability, storage, and communication resources as a utility [53]. Different types of cloud computing exists (public, private, hybrid, etc.)…”