## 8 Generalization and Cross-Domain Applicability  
The architecture and methodologies implemented in our solution are incredibly versatile and show great promise for widespread adoption across a variety of domains in e-services that must adhere to EU legislation and GDPR compliance. Due to the multi-dimensional nature of our development, it can be utilized as either a standalone solution or as individual solutions.  
Our identity management system could be utilized in cases that require strong authentication and a high level of assurance. For example, it could be used in educational institutions to securely manage student and staff identities, enforce role-based access control, and protect sensitive academic or personal information. The p-ABC credential can be used multiple times to generate unlinkable privacy-preserving crypto-tokens, allowing the user to interact with the relying party offline. This is particularly useful for scenarios like IoT or face-to-face interactions, where short-range wireless communication (such as Bluetooth or NFC) is used, enabling privacy-preserving interactions even in real-time environments. Additionally, the selective disclosure feature proves advantageous in scenarios where users wish to share only necessary information. It is imperative that users are informed and provide consent when their data is disclosed. Our client app and dashboard have incorporated these legislative restrictions to ensure compliance with GDPR regulations, which are applicable to a wide range of e-services. This can be applied in scenarios such as obtaining a driver's license or purchasing alcohol, where users are required to disclose either their license or simply their age.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Centralized**} & \multicolumn{3}{c}{**Individual**} & \multicolumn{3}{c}{**Federated**} \\ \hline
**AUC-ROC** & **SIREOS** & **AP** & **AUC-ROC** & **SIREOS** & **AP** & **AUC-ROC** & **SIREOS** & **AP** \\ \hline
0.7889 & 0.0513 & 0.7498 & 0.8 & 0.05 & 0.74 & 0.7925 & 0.050 & 0.7462 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Machine learning model results: individual, centralized, and federated settings.  
Moreover, our identity management system could be well suited for IoT environments, as its architectural design and lightweight cryptographic mechanisms can effectively support device-to-device interactions, even in offline scenarios involving resource-constrained IoT devices.  
Our FL procedure could also be applied in various domains where data privacy and decentralization are critical, such as healthcare, education, finance, and IoT. Its privacy-preserving design and secure aggregation make it ideal for scenarios involving sensitive, distributed data, ranging from hospital diagnostics to personalized education and collaborative cybersecurity.  
Of course, our approach can be deployed as a standalone solution across a range of domains that require secure access management, distributed data analysis, and personalization. It is applicable in healthcare and educational environments where protection of sensitive information is critical, in finance for privacy-compliant identity verification, in smart cities and IoT networks for secure device authentication and collaborative learning, and in government services for anonymous verifiable citizen interactions.  
In summary, the challenges addressed in our use case--privacy-sensitive data, multi-role access, regulatory compliance, and decentralized operation--are not exclusive to pediatric mental health. They are representative of broader societal needs, making our solution adaptable and valuable for a wide spectrum of real-world applications.