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A Maturity Model for the Enforcement of PETs in Federated Settings
Hammam Abu Attieh1, Mehmed Halilovic1, Marius de Arruda Botelho Herr2
1Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Health Data Science, Charitéplatz 1, 10117 Berlin, Germany.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
We introduce a Privacy-Enhancing Technology (PET) Integration Maturity Model for federated learning platforms like FLAME. This model enhances privacy usability and enforcement by shifting responsibility from analysts to the platform.
Area of Science:
- Computer Science
- Information Security
- Machine Learning
Background:
- Federated learning (FL) platforms require robust privacy measures.
- Ensuring usability and enforceability of privacy in FL is challenging.
- The German PrivateAIM project developed the FLAME platform.
Purpose of the Study:
- To propose a framework for integrating privacy-enhancing technologies (PETs) into federated learning.
- To enhance the usability and enforceability of privacy within the FLAME platform.
- To define maturity levels for PET integration.
Main Methods:
- Development of a three-level PET Integration Maturity Model: Analysis-Based, Library-Based, and System-Based.
- Analysis of how responsibility for PET application shifts across maturity levels.
- Evaluation of the impact of maturity levels on auditability and consistent privacy enforcement.
Main Results:
- The proposed model categorizes PET integration into three distinct maturity levels.
- Higher maturity levels demonstrate a shift of PET responsibility from users to the platform.
- Increased maturity correlates with enhanced auditability and consistent privacy enforcement across distributed sites.
Conclusions:
- The PET Integration Maturity Model provides a structured approach to improving privacy in federated learning.
- The FLAME platform can leverage this model to enhance privacy usability and enforcement.
- Adopting higher maturity levels facilitates more robust and auditable privacy protection in federated learning systems.
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