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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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An Open Source Benchmark for Pseudonymization Services in Translational Research.

Armin Müller¹, Felix Nikolaus Wirth¹, Fabian Prasser¹

¹Medical Informatics Group, Center of Health Data Sciences, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

This summary is machine-generated.

A new open-source tool benchmarks pseudonymization services for healthcare data, addressing scalability needs in research data platforms. It simulates realistic workloads to assess performance for secondary data use.

Keywords:

Benchmarking Data privacy Data sharing Health data Performance evaluation Pseudonymization

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Area of Science:

Health Informatics
Data Privacy
Software Engineering

Background:

Pseudonymization is crucial for secondary use of healthcare data, particularly in research.
Existing methods lack systematic performance assessments for scalable pseudonymization services.
High scalability is essential for large datasets in research data platforms.

Purpose of the Study:

To develop an open-source benchmarking tool for pseudonymization services.
To enable systematic performance assessments of pseudonymization tools.
To support scalable pseudonymization for secondary data use.

Main Methods:

Developed an open-source benchmarking tool simulating real-world workloads.
Configurable request distributions support diverse scenarios (read-heavy, write-heavy).
Incorporated multi-threading, automated authentication, and identifier handling.

Main Results:

The tool provides realistic performance analyses, including network factors.
It supports continuous delivery pipelines for pseudonymization services.
Modular connector design allows for benchmarking new services and adaptability.

Conclusions:

The developed tool addresses the lack of systematic performance assessments for pseudonymization services.
It facilitates realistic performance analysis and supports scalable secondary data use.
The open-source nature and modular design promote adaptability and integration.