Future Generation Computer Systems

FAIR and Square: Privacy Compliance Framework for Healthcare Databases

Initial evaluations using various machine-learning algorithms on pre-and post-generalized datasets demonstrate the privacy framework’s effectiveness in mitigating privacy risks while preserving data usability.

From Privacy to Data Erasure: A Review of New Rights and Emerging Challenges in the Era of Electronic Health Records

The digitalization of healthcare systems has transformed the production, storage, and sharing of clinical information. While electronic health records (EHRs) enhance care efficiency, accessibility, and continuity, they simultaneously introduce complex ethical, legal, and cybersecurity challenges that directly affect patient interests. This narrative review examines the emerging rights associated with digital health records, particularly the right to privacy and the right to be forgotten, alongside threats to confidentiality, cybersecurity, and patient safety. A comprehensive literature search was conducted across PubMed, Web of Science, MEDLINE, and the Cochrane Library. First, the right to be forgotten appears particularly relevant for oncological patients facing financial discrimination and for individuals asserting gender identity rights, yet it cannot be unconditionally extended to genetic data, given its relevance to relatives and future generations. Second, confidentiality risks are amplified by re-identification vulnerabilities, unauthorized access by personnel, and the broad connectivity of digital systems. Third, the secondary use of data from EHRs, including artificial intelligence (AI) integration, commercial exploitation, and large language model training, raises substantial privacy concerns. Fourth, ransomware, phishing, and data breaches can erode patient trust. In this article, we analyze these issues within the evolving European regulatory framework, highlighting the tension between individual privacy rights and broader public interests. We argue that robust data protection must be balanced with scientific progress and that this requires opt-in consent frameworks, staff training, and transparent AI governance.

Privacy-Enhancing Technologies and Privacy-By-Design for Medical AI

This paper examines how privacy-enhancing technologies such as synthetic data, federated learning and ‘Secure Data Environments’ can be integrated into artificial intelligence ( AI ) development processes to uphold key data protection principles in the UK GDPR , like storage limitation, data minimisation, purpose limitation, security, and fairness. The analysis highlights how privacy-enhancing technologies offer benefits beyond anonymisation by embedding privacy-by-design values to support responsible innovation and protect sensitive patient data throughout the design, training, and validation of medical AI systems. The paper uses the 2015 DeepMind and Royal Free case as a practical study to realise the practical and legal benefits of privacy-enhancing technologies in medical AI development, particularly involving public-private collaborations. While grounded in the UK context, the findings have broader relevance to the European Union and other international jurisdictions grappling with tensions between data protection and AI development in the healthcare context.

Integrating Multi-Party Computation in Data Warehouses for Secure Healthcare Analytics: An Architectural and Performance Study

BETTER: Better rEal-world healTh-DaTa distributEd analytics Research platform: project progress and emerging research challenges

Challenges and Advances in Arduino- Enabled Healthcare Information Systems: Systematic Review

Arduino is a popular choice for healthcare technology due to its open-source platform and low cost, making it ideal for developing intelligent medical solutions without the high costs of conventional systems. The current studies have largely assessed Arduino capabilities based on specific features rather than thoroughly testing the system's security, technical, and operational aspects. To address these issues, this study uses a Population, Intervention, Comparison, and Outcome (PICO)-based SLR that adheres to PRISMA guidelines and the Joanna Briggs Institute tool to critically evaluate the study's findings. This study collected literature from major databases, including Web of Science, Google Scholar, and ScienceDirect, covering the period from 2020 to 2024. It identified 216 papers, and only 8 met the PICOS eligibility criteria. The findings reveal that all researchers addressed security aspects, such as basic authentication and privacy, but they neglected cryptographic measures, which remain the weakest point of execution. Encryption, auditability, accountability, and freshness were implemented only once. Integrity, authorization, and third-party protection were rarely applied, and non-repudiation evaluation was minimal. Technical aspects such as accuracy and validation were satisfactory. However, reliability was tested in limited environments, and fault tolerance and robustness were poorly implemented. Operational aspects like cost and sustainability were widely addressed, whereas resilience, availability, and reliability received little attention. Arduino-based medical systems show strong technical promise but remain constrained by major security and operational gaps, limiting their readiness for real-world health settings.