The Advanced Confidentiality Engine as a Scalable Tool for the Pseudonymization of Biomedical Data in Translational...

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.