## 3 Related Work
### Adoption of PETs in Healthcare  
The integration of PETs in healthcare applications seems promising, as these primitives are designed to prevent data leaks while striking a balance between privacy and usability [12]. However, their practical applicability faces several challenges. Challenges include inconsistent standards, lack of real-world validation, and trade-offs between privacy and data utility. Moreover, no single PET offers a complete solution; effective implementation requires context, specific combinations and expert judgment [12]. The work in [13] examined the development of existing PETs in various fields and whether they comply with legal principles and privacy standards and reduce the threats to privacy in IoT-related environments. The authors conclude that PETs remain in the early stages of technological readiness, with effectiveness varying across different threat types. Their computational complexity impacts usability, while adoption is influenced by cost and user-friendliness. Additionally, legal compliance requires adherence to regulations like GDPR, and integration challenges persist, particularly with existing systems. Notably, even in the recent work in [14], it is determined that the maturity of these technologies still lags, except the technology of Federated Learning specifically for healthcare settings, overcoming the problem of health data leakage. The analysis of [15], specifically for the healthcare sector, indicates that Privacy-Attribute-Based Credentials (p-ABCs) are suitable tools for every smart health scenario. By utilizing p-ABCs, individuals can selectively disclose specific details about their identity, enhancing their privacy and security. Moreover, the design of these credentials ensures that different uses of the same credential cannot be linked together, safeguarding user privacy, even in the event of collusion among multiple malicious entities, and supporting distributed issuance (dp-ABC) [16]. P-ABCs were also utilized for privacy-preserving authentication in solutions like uProve [17], Identity Mixer [18], and elPASSO [19]. Solutions like uProve and Identity Mixer have been developed to offer privacy advantages over conventional SSO systems, while projects such as ABC4 Trust [20] and ARIES [21] use p-ABCs to improve user privacy and data control. Identity systems like Sovrin, uPort, and Jolocom face several drawbacks related to their identity management systems. However, the aforementioned solutions rely on a single identity provider, making them vulnerable to single-point-of-failure attacks and third-party control, and threatening privacy since user behavior is exposed to the identity provider. Blockchain-based electronic health records systems, such as [22; 23; 24; 25], while they were assumed to rely on a decentralized IdM, they rely on a centralized IdM of healthcare institutions [26]. Therefore, the implementation of a decentralized IdM, which enables users to control their digital identities independently from service providers [27], or identity providers is crucial in the healthcare sector. Table 1 outlines key differences among various identity management systems, comparing them in terms of privacy, user autonomy, and technical architecture. Moreover, the adoption of blockchain technology in the healthcare sector is steadily increasing. Further, it is often combined with privacy-preserving techniques, like cryptography and machine learning, to reduce risks such as linkability, traceability, and identity exposure [28]. However, according to the recent systematic review in [28], although these methods enhance privacy, they primarily offer pseudonymization rather than full anonymization, and they are often hindered by high computational overhead and limited scalability, posing challenges for large-scale deployment in healthcare systems. In our solution, the blockchain's role is primarily to support verifiable credential issuance and revocation, ensuring integrity without exposing sensitive user data.