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The Advanced Confidentiality Engine as a Scalable Tool for the Pseudonymization of Biomedical Data in Translational
Armin Müller1, Eric Wündisch2, Felix Nikolaus Wirth1
1Medical Informatics Group, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
The Advanced Confidentiality Engine (ACE) is a new, scalable open-source service for pseudonymization, crucial for translational research data protection. It efficiently manages links between identifying and research data, supporting millions of records.
Area of Science:
- Biomedical research and translational science
- Data privacy and security in research
Background:
- Pseudonymization is vital for protecting sensitive data in translational research, enabling data sharing while adhering to privacy regulations.
- Integrating pseudonymization into high-throughput workflows is challenging, with limited open-source solutions available for large-scale data processing.
Purpose of the Study:
- Introduce the Advanced Confidentiality Engine (ACE), a scalable, open-source pseudonymization service.
- Focus on ACE's capability to create and manage secure links between personal identifying information and research data.
Main Methods:
- ACE features a lean architecture with a compact database schema, modern open-source technologies, and a RESTful API.
- Includes fine-grained access control, domain-based pseudonym structuring with inheritance, and a comprehensive audit trail.
- Evaluated ACE's scalability under diverse workload scenarios.
Main Results:
- ACE supports 9 pseudonymization algorithms, various encoding options, and metadata annotation with inheritance.
- The service enables both pseudonymization and depseudonymization with individual access control.
- Demonstrated scalability, handling approximately 6000 transactions per second across different workloads.
Conclusions:
- ACE is a modern, highly scalable open-source pseudonymization service designed for biomedical research needs.
- It offers a valuable solution for institutions building or enhancing their translational data infrastructure.
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