## 5 System Description
### Architecture  
Our advanced cryptographic techniques allow moving from a centralized to a distributed identity provider model. The proposed system architecture offers a secure, privacy-preserving, and modular identity management solution. It divides the operation into two distinct phases: user authentication and the issuance of access authorization, either as a one-time token or a reusable privacy-preserving credential. The system operates by distributing tasks across multiple independent servers through advanced cryptographic techniques. This approach effectively minimizes reliance on individual components and user devices, enhancing overall security. However, it is essential for both the client app and relying party web app to establish trusted communication in order to facilitate this process. To accommodate different application scenarios, the architecture supports two modes of operation: one that generates distributed tokens for immediate use and another that issues credentials allowing users to derive access proofs offline. This flexible design is compatible with widely adopted standards like OAuth and SAML, making it suitable for modern, scalable, and user-centric identity systems.  
The Federated Learning architecture adopted in our system is designed to uphold data privacy while enabling collaborative model training. It introduces a decentralized machine learning paradigm where data remains local, and only model updates are shared. To address the critical issue of data availability, especially relevant in settings with shared devices, we augment the architecture with encrypted data storage mechanisms.  
A critical challenge lies in selecting the appropriate technology and evaluating its suitability for existing e-services. Key considerations include (1) the effort required to establish a trusted connection between users and healthcare providers; (2) whether blockchain-based solutions can enhance the efficiency of privacy-preserving technologies; and (3) if AI models trained via Federated Learning, while preserving privacy, can still maintain diagnostic accuracy for patients.  
Our proposed model addresses these challenges by integrating the following:  
* Blockchain-based virtual Identity Providers (v-IDPs) to enable decentralized and tamper-proof identity management;
* Privacy-Attribute-Based Credentials (p-ABCs) to ensure anonymity and selective disclosure, grounded in established mathematical models introduces in [66; 67], and
* Federated Learning for secure, distributed analysis of smart device data;
* Relying Parties (e.g., hospitals, pharmacies, insurers) interact with this framework through cryptographic protocols enforced by the distributed ledger, specifically:
* Authentication and Authorization: Relying parties verify user credentials via zero-knowledge proofs (ZKPs) and smart contracts, eliminating the need for direct data exposure.
* Immutable Audit Trails: All transactions (e.g., access requests, credential issuance) are recorded on-chain, providing transparency while preserving privacy through pseudonymization.
* Interoperability: The blockchain acts as a universal trust layer, allowing relying parties to independently validate proofs (e.g., age, insurance status) without centralized coordination.  
By combining these components, our system not only mitigates the drawbacks of siloed architectures (e.g., single points of failure, privacy breaches) but also demonstrates how decentralized technologies can improve the security, efficiency, and user control of healthcare e-services.