An Open Source Benchmark for Pseudonymization Services in Translational Research
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View abstract on PubMed
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.
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.
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