## 2 Related Work
### Distributed Analytics  
DA constitutes a paradigm shift in conventional data analysis [7; 9; 10; 11; 12; 13; 14; 15]. At its core, approaches for DA perform data analysis decentrally. This means that analysis-ready data sets stay within institutional borders, and only the analysis code and its analysis results are transmitted between each institution. Finally, after analysing each data set, the aggregated analysis results return to the analyst, and from the obtained information, insights can be derived. The analysis can include basic statistics, queries, or even complex Machine Learning (ML) training routines. Besides the differences in the used technology, DA implementations can differ in their execution policies. There exists an incremental or a parallel execution of the analysis [13]. For the first approach, the involved data premises are set in succession, and the analysis is sent from one institution to the other. For the latter one, replicas of the analysis are sent to each institution and executed simultaneously. 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]. One of such approaches is DataSHIELD (DS), which is client-server based [20; 23]. DS uses a custom R-library to execute the analysis requests decentrally. DS has been applied to multiple use cases already ([[https://www.datashield.org/about/publications](https://www.datashield.org/about/publications)]([https://www.datashield.org/about/publications](https://www.datashield.org/about/publications)), accessed on 18 February 2022). Second, secure multi-party computation (SMPC) has attracted attention due to its security guarantees [21]. Instead of libraries, SMPC is protocol and encryption-based. 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]. The PHT is an infrastructure for privacy-preserving DA of patient-related health data. Figure 1 shows a high-level overview of the PHT. It consists of multiple components that can be roughly classified into centralised and client-side components. Clients--the so-called stations--are typically, but not necessarily, located in secured environments, i.e., behind a firewall at a specific institution which can be a research organization or even a company. Note that each institution can install as many stations as they want. The stations communicate with centralised components to retrieve analytical tasks, which are then executed on the client-side. Each station itself has access to the patient data, which is ideally already virtually integrated and homogenised. In Germany, patient data is currently homogenised based on the HL7 FHIR standard ([[https://www.hl7.org/fhir/](https://www.hl7.org/fhir/)]([https://www.hl7.org/fhir/](https://www.hl7.org/fhir/)), accessed on 18 February 2022) within the large Medical Informatics Initiative ([[https://www.medizininformatik-initiative.de/en/start](https://www.medizininformatik-initiative.de/en/start)]([https://www.medizininformatik-initiative.de/en/start](https://www.medizininformatik-initiative.de/en/start)), accessed on 18 February 2022). After an analysis ends, the station sends the results to the central components from which the next station can retrieve both the analysis algorithm (or better: script/program) and the intermediate results provided by previous stations. Usually, PHT ecosystems are based on _containerised_ applications using OCI-compliant ([[https://opencontainers.org](https://opencontainers.org)]([https://opencontainers.org](https://opencontainers.org)), accessed on 18 February 2022) technologies such as Docker ([[https://www.docker.com](https://www.docker.com)]([https://www.docker.com](https://www.docker.com)), accessed on 18 February 2022) [19; 22]. Therefore, the train represents an image encapsulating the algorithm code.