Aug 2026· European Journal of Health Law· pp. 1-26· 0 citations
TL;DR
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.
Abstract
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.
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.
Ze-Yang Zhu, Matthias N. Louws, Roland V. Bumbuc et al.· 0 citations
This study aims to guide researchers and practitioners in designing secure, efficient, and privacy-preserving AI systems for healthcare by proposing a structured classification of privacy-preserving methods into four main categories: cryptographic approaches, decentralized learning methods, perturbation-based techniques, and hybrid models.
Chaima Bejaoui, Mohamed Hadded, Hakim Ghazzai et al.· Cluster Computing· 0 citations
Background Biomedical artificial intelligence (AI) requires the integration of privacy-enhancing technologies (PETs) to safeguard sensitive clinical, imaging, and genomic data while preserving analytical utility. Objectives This review critically and systematically maps applications of PETs across the biomedical AI lifecycle in accordance with PRISMA-ScR guidelines and evaluates their technical trade-offs, deployment feasibility, and residual risks. Methods We systematically searched PubMed, IEEE Xplore, ACM Digital Library, and Scopus for studies published between 2015 and 2025. Eligible studies addressed differential privacy, federated learning, secure multiparty computation, homomorphic encryption, or hybrid approaches in biomedical AI. Data were charted on PET type, modality, lifecycle stage, utility metrics, privacy parameters, and deployment considerations. A critical appraisal rubric assessed threat-model adequacy, methodological clarity, reproducibility, privacy–utility transparency, and deployment realism. Additionally, we hand-searched major venues (USENIX Security, NeurIPS, AAAI) and screened Google Scholar for grey literature, applying de-duplication across sources. Results We identified 87 studies spanning clinical decision support, genomics, and medical imaging. From 25,761 initial records, 3,754 underwent title/abstract screening and 1,968 underwent full-text assessment. PETs demonstrated distinct strengths and limitations: differential privacy provided provable guarantees but reduced performance on imbalanced data; federated learning improved data access but remained vulnerable to gradient leakage; and cryptographic methods ensured confidentiality at high computational cost. Synthetic data generation supported privacy-conscious data sharing and benchmarking but remained sensitive to disclosure risk, fidelity loss, and subgroup representation. Hybrid and emerging approaches, including trusted execution environments, zero-knowledge proofs, and privacy-preserving transformer architectures, mitigated composability gaps yet lacked full end-to-end assurance. Case studies at hospital and biobank scale illustrated practical feasibility and infrastructure demands. Conclusions Situating PETs within technical and operational contexts clarifies their capabilities, limitations, and deployment challenges. Residual risks persist, including fairness concerns, inference-time leakage, and overreliance on PETs as compliance proxies. Sustained technical innovation and institutional governance remain essential for the trustworthy integration of PETs in biomedical AI.
Seha Ay, Umit Topaloglu, Wei Zhang· Frontiers in Digital Health· 0 citations
This work expands upon the privacy threat assessment model to quantitatively evaluate the risks of data likability, identifiability, non-repudiation, detectability, unintended disclosure, indulgence, and policy & consent noncompliance, and constructs a framework aimed at mitigating these identified risks.
Jamila Alsayed Kassem, Tim Müller, Christopher A. Esterhuyse et al.· 0 citations
The rapid advancement of Artificial Intelligence (AI) and Big Data technologies has transformed nearly every aspect of human life, including healthcare, education, governance, finance, law enforcement, and communication. These technologies have significantly improved decision-making processes, increased efficiency, enhanced public service delivery, and facilitated innovation across industries. However, alongside these benefits, AI and Big Data have introduced new challenges concerning the protection of fundamental human rights. Issues such as privacy violations, algorithmic discrimination, mass surveillance, data misuse, misinformation, lack of transparency, digital exclusion, and threats to freedom of expression have become major global concerns. AI systems increasingly influence employment opportunities, judicial decisions, healthcare diagnoses, financial services, and public administration, making fairness, accountability, and ethical governance essential. Human rights principles such as equality, dignity, autonomy, privacy, and non-discrimination must therefore be integrated into the design, deployment, and regulation of intelligent technologies. Governments, international organizations, technology companies, civil society, and academic institutions are actively working toward developing responsible AI frameworks, ethical guidelines, and legal regulations to ensure that technological innovation remains aligned with democratic values and international human rights standards. The emergence of explainable AI, privacy-preserving machine learning, algorithmic auditing, and human-centered AI reflects growing efforts to balance innovation with accountability. This study examines the relationship between AI, Big Data, and human rights by exploring both opportunities and challenges. It discusses privacy protection, ethical governance, algorithmic fairness, legal frameworks, digital inclusion, cybersecurity, and future policy directions. The paper concludes that sustainable technological development requires collaborative governance, transparent algorithms, robust legal protections, and continuous public awareness to safeguard human rights in the digital era.
Research Author· Global Journal of Computing...· 0 citations
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