Decentralized databases have gained popularity in the last few years in different areas, such as: traceability, supply chains or finance. Leveraging this type of emerging technology will improve knowledge sharing, as well as the transparency and traceability of the data for digital systems. In a similar way, the characteristics are advertised by the centralized ledger technologies, which are manufactured by large cloud service providers such as Amazon. The present study analyzes the performance of two ledger technologies: BigchainDB (i.e., the decentralized blockchain database) and Amazon QLDB (i.e., the centralized ledger database with transparent and immutable characteristics). For the purposes of comparison, we have integrated these technologies into our traceability platform, which is called the Smart Tracking Platform (STP), and performed a series of experiments enabling us to acquire data for different metrics, such as CPU or memory usage for both the reading and writing operations. The findings of the present study show that QLDB has an overall better performance compared to BigchainDB, based on the metrics that have been considered. From the perspective of database ledger implementation, Amazon QLDB proved to be an integrated solution, easier to use, while BigchainDB comprises a more complex system to be implemented and developed, but is more flexible. Although both systems are almost ready to use solutions for local environments, when it comes to configuration and setting up the communication between nodes within a production environment, BigchainDB adds a layer of complexity from a DevOps perspective, while Amazon QLDB completely overcomes it. Depending on the area considered and the identified needs, both BigchainDB and Amazon QLDB can be considered as suitable solutions for a ledger database.

Personal health records (PHRs) are digital repositories that allow individuals to access, manage, and share their health information. By enabling patients to track their health over time and communicate effectively with healthcare providers, personal health records support more personalized care and improve the quality of healthcare. Their integration with emerging technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain, enhances the utility and security of health data management, facilitating continuous health monitoring, automated decision support, and secure, decentralized data exchange. Despite their potential, PHR systems face significant challenges, including privacy concerns, security issues, and digital accessibility problems. This paper discusses the fundamental concepts, requirements, system architectures, and data sources that underpin modern PHR implementations, highlighting how they enable continuous health monitoring through the integration of wearable sensors; mobile health applications; and IoT-enabled medical devices that collect, process, and transmit data to support proactive care and personalized treatments. The benefits and limitations of PHR systems are also discussed in detail, with a focus on interoperability, adoption drivers, and the role of advanced technologies in supporting the development of secure and scalable health information systems for a smarter world.

Fragmentation across consumer wearables, hospital messaging systems, telehealth platforms, and cloud services impedes timely data reuse and increases privacy risks, for example through proprietary telemetry, legacy HL7v2 messages, intermittent home connectivity, and inconsistent consent enforcement. To design and evaluate a privacy-centric microservice framework that integrates heterogeneous medical data into Fast Healthcare Interoperability Resources (FHIR) while sustaining sub-second end-to-dashboard latency under realistic network conditions. We followed a security- and privacy-by-design process inspired by secure software development lifecycles, consolidating requirements from the GDPR, EHDS Regulation, and Zero Trust guidance into an explicit threat model. We built container-native services orchestrated with Kubernetes and enforced a Zero Trust posture using device-bound X.509 certificates and mutual TLS. A hot-swappable adapter layer normalized HL7v2/IEEE 11073/proprietary telemetry to FHIR resources and persisted them in PostgreSQL with a row-level security. Performance tests replayed wearable sessions through 50 concurrent Android emulators over Wi-Fi and 4G and a 5% random loss model. The outcomes were processing, ingestion, end-to-dashboard latencies, throughput, and resource utilization. Security probes assessed credential replay/clone, injection attempts, horizontal privilege escalation, and volumetric DoS. The processing interval from NIC reception to storage remained below 100 ms (95th percentile). On 4G (RTT ≈ 75 ms), the ingestion latency was 132 ms; with 5% random loss, the end-to-dashboard The security controls blocked credential replay/clone attempts, neutralized injected SQL, enforced row-level isolation, and reduced the observed request rate in an illustrative 50,000 SYN/s availability probe. These results represent an initial validation rather than a full penetration test; large-scale clinical deployments will require independent security assessments. The outcome is a Zero Trust FHIR reference implementation with an explicit threat model, device-bound enrollment, adapter-based normalization, row-level isolation, observability, and targeted validation. A zero-trust microservice architecture can deliver EHDS-aligned privacy controls and real-time analytics without compromising performance. Beyond the reference implementation, the work contributes a structured security- and privacy-by-design methodology and explicit threat model that can guide similar digital-health platforms.