Whole population cohorts vs sampled comparators designs for evaluating health and educational outcomes of children with inborn rare conditions: a simulation study.

The rapid expansion of IoT devices in maternal health monitoring enables continuous data collection and improved clinical assessment; however, it also introduces significant security and privacy concerns due to the sensitivity of maternal health information. This study investigates how artificial intelligence (AI) and machine learning (ML) can enhance both analytical performance and data protection in IoT-based maternal monitoring systems. The proposed framework employs Random Forest, Decision Tree, Support Vector Machine, and a stacking–bagging ensemble to improve maternal risk prediction and anomaly detection. Privacy-preserving techniques are integrated to secure physiological parameters: homomorphic encryption ensures data confidentiality during processing, while differential privacy limits information leakage from model outputs. Experimental results show that the stacking classifier combined with Random Forest achieved the highest accuracy of 82.3%, demonstrating greater robustness than traditional algorithms. Although differential privacy strengthened data protection, it reduced precision and F1-score, highlighting a trade-off between privacy and accuracy. Overall, integrating ensemble learning with privacy-preserving methods improves the security, accuracy, and reliability of IoT-driven maternal health monitoring systems.

Artificial Intelligence (AI)-driven hyper-personalisation has reconfigured digital marketing by embedding predictive analytics and automated decision-making into everyday consumer interactions. While these systems enhance marketing efficiency and engagement, they simultaneously intensify concerns regarding surveillance, behavioural manipulation, and erosion of consumer autonomy. Existing global scholarship recognises these risks; however, limited context-specific analysis interrogates how such dynamics unfold within emerging regulatory environments. This study addresses that gap by critically examining how AI-driven hyper-personalisation reshapes consumer privacy in South Africa and evaluating the adequacy of its regulatory framework in comparison with international standards such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). A desktop-based systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to synthesise peer-reviewed studies published between 2019 and 2025. The findings reveal that AI-driven personalisation systems operate through opaque algorithmic profiling mechanisms that weaken informed consent and shift control away from consumers. Although South Africa’s Protection of Personal Information Act (POPIA) establishes foundational data protection principles, it lacks AI-specific provisions addressing explainability, automated decision-making, and effective consumer redress. In contrast, while the GDPR and CCPA provide more explicit safeguards, enforcement and technical interpretability challenges persist even within these jurisdictions. The study argues that the regulatory disparity between South Africa and more mature digital economies exposes local consumers to heightened risks within transnational digital marketing ecosystems. It concludes that strengthening AI-specific governance, enhancing institutional audit capacity, and embedding ethical design principles are necessary to reconcile technological innovation with consumer rights in both South African and global contexts.

In the latest years, there has been an increasing tingrend for police forces and judicial authorities to employ predictive profiling technologies in justice and law enforcement, posing major risks to fundamental rights of citizens. These systems create ontologies, perform risk assessment, and even predict the probability of re-offense. However, there is a lack of transparency in how those systems are trained, tested, validated and employed, and research has shown that they are biased against marginalized groups. The EU Artificial Intelligence Act, by introducing a new legal basis for the processing of special categories of personal data without appropriate safeguards, and without a coherent legal framework with the Law Enforcement Directive, creates a source of potential issues detrimental to data subjects’ rights and freedoms. This article explores the intersection between the AI Act and the LED Directive, highlighting gaps and inconsistencies in the related legal framework.

Sharing IP addresses with law enforcement authorities: data protection in cybersecurity-related public-private partnerships

Exploring interpretative fragmentation in EU data protection impact assessments — quantitative analysis from COVID-19 proximity apps

Targeting homologous recombination deficiency with intensified chemotherapy versus standard chemotherapy followed by olaparib in stage III breast cancer (SUBITO): an open-label, randomised, controlled, phase 3 trial.

Dynamic memdiode–driven Hopfield neural network for 3D CT encryption in IoMT

A watermarking-based multimodal healthcare data protection using blockchain and deep learning

A digital trace is the sum of behavior, transaction, and access data left by an individual or organization in digital systems. With digitalization, these traces have become central to tax authorities' audit tools. Digital trace-based tax audits offer a more analytical, comprehensive, and data-driven approach compared to traditional audit methods. Applications such as electronic records, electronic invoices, and electronic ledgers provide significant advantages in terms of efficiency, accuracy, and speed in audit processes. However, this situation raises new legal debates regarding the protection of personal data, privacy, and the right to confidentiality. The purpose of this study is to evaluate the use of digital traces in tax audits in Turkey in terms of data privacy. The study approaches the use of digital traces in tax audits from two angles: on the one hand, it explains the legal framework of the audit, and on the other hand, it examines the limits regarding the protection of data privacy. As a result, it has been revealed that for digital auditing to be successful, a balance must be struck between protecting legal limits and protecting privacy.

The emergence of the Unified Payments Interface (UPI) has transformed the digital payment ecosystem by enabling seamless, instant, and interoperable financial transactions across India. Its widespread acceptance has accelerated the shift toward cashless payments and increased the availability of digital financial services for millions of users. However, the rapid growth in transaction volume and user adoption has also created new opportunities for cybercriminals to exploit vulnerabilities within the digital payment infrastructure.Financial fraud associated with UPI platforms has become increasingly sophisticated, involving techniques such as phishing campaigns, fraudulent QR codes, identity impersonation, account hijacking, social engineering attacks, and the misuse of mule accounts. These evolving threats generate complex transaction patterns that are often difficult to detect using conventional rule-based security mechanisms. As fraud strategies continue to change, static detection systems struggle to provide accurate and timely identification of suspicious activities.Recent progress in Artificial Intelligence, Machine Learning, and Deep Learning technologies has significantly enhanced the capability of fraud detection systems. One of these developments has caught a lot of interest from researchers is hybrid deep learning methods that are able to integrate the benefits of several computational models. Several techniques can be combined in a unified approach for enhancing fraud detection, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Autoencoders, Graph Neural Networks (GNNs), Explainable Artificial Intelligence (XAI), and Federated Learning. These architectures enable real-time analysis, adaptive learning, privacy protection, and efficient management of large-scale transactional data. This review is based on the recent research and studies regarding the detection of fraudulent activities in UPI-based payment environment using hybrid deep learning techniques. It offers detailed evaluations of the current models, their strengths and weaknesses, explores current issues and challenges, and suggests areas of future work. These results suggest that hybrid deep learning architectures are promising to improve the security of digital payment systems through better detection accuracy, fewer false alarms, safeguarding sensitive user data, and greater transparency of automated decision-making. Thus, these wise frameworks are a valuable basis for developing safe, reliable, and trustworthy digital financial ecosystems

Routinely collected healthcare data has the potential to transform healthcare. Initiatives such as the Born in Bradford study and the UK's COVID-19 response show how routine data research can improve and save lives. Within palliative care, there is scope for such research to improve understanding of care provision, avoid unnecessary service use and reduce inequalities, while minimising burden on research participants.The legal and ethical obligations surrounding such research are considerable, and it is important for those designing, conducting and interpreting routine data research to understand these obligations and to contribute to the development of future research structures. The UK General Data Protection Regulations (GDPR) and Common Law Duty of Confidentiality underpin the legal and ethical frameworks surrounding routine data research.The legal and ethical obligations surrounding such research are considerable, and it is important for those designing, conducting and interpreting routine data research to understand these obligations and contribute to the development of future research structures. The UK General Data Protection Regulations (GDPR) and Common Law Duty of Confidentiality underpin the legal and ethical frameworks surrounding routine data research.

Palliative care has been identified as one of the most inequitable areas of healthcare. In Switzerland, as globally, disparities in end-of-life care (EOLC) exist along socio-demographic lines, shaping the access to and quality of care received across services by patients and their caregivers. Research has linked these disparities to binary gender differences and other aspects of a person's social position. The GiveCare project aims to provide a systemic understanding of how aspects of gender and diversity intersect to shape the provision of EOLC in Switzerland and to translate the generated knowledge into practice, policy, education and training. GiveCare employs a sequential mixed-methods design, combining a feminist intersectional approach with a systems thinking lens. It consists of four work packages (WPs): (1) A national survey will assess the perceived awareness of gender and diversity among palliative care professionals. (2) A focused ethnography will provide in-depth insights into the care journeys of patients and their significant others in two specialised inpatient palliative care units. (3) A social network analysis and discrete event modelling will unpack the complexity of such care journeys across inpatient and outpatient settings in the Canton of Zurich. 4. Throughout the process, an integrated knowledge translation approach will help to generate actionable evidence, co-created with patients, caregivers, professionals and policymakers, to enhance inclusivity and equity in EOLC practice and policy. The Ethics Committee of the Canton of Zurich, Switzerland, granted ethical approval for WP2-4 (Req-2025-02241) and issued a waiver for WP1 (Req-2025-00211). The research team will conduct the research in accordance with the Swiss Federal Act on Data Protection and the rules and regulations of the Swiss Federal Data Protection and Information Commissioner. The findings will be disseminated through peer-reviewed publications and conference presentations as well as via the community of practice established through the integrated knowledge translation process.

This article examines the pressing issue of developing a secure, specialized online platform for distance learning for children with special educational needs. The digitalization of education has opened up new opportunities for inclusion, but mainstream solutions often fail to address the specific needs of this category of students, creating digital, cognitive, and social barriers. The goal of the study is to develop a conceptual model of a secure and adaptive educational environment that comprehensively addresses accessibility, personalization, and cybersecurity. The project took into account the usability of children with various developmental disabilities, including sensory impairments, autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD), and based on this, the key principles of user interface and user experience (UI/UX) design were formulated. The proposed platform architecture includes an intelligent content and interface adaptation system, personalized learning paths, and secure communication modules with pre-moderation functions. Particular attention is paid to a multi-layered security system that ensures the protection of personal data, the prevention of cyberbullying, and access control. The article is of practical value to educational technology developers, educators, and administrators of educational institutions seeking to create an inclusive digital learning environment.

ABSTRACT The empirical literature concerning the joint analysis of interactions between information and communication technologies (ICTs), financial inclusion and economic growth, particularly in the context of the countries of the West African Economic and Monetary Union (WAEMU), is still underdeveloped. This study aims to fill this gap by assessing the impact of ICT on economic growth, considering financial inclusion as the main transmission mechanism. The analysis is based on a panel of seven WAEMU countries covering the period 2006–2023 and uses a simultaneous equation model estimated via the triple least squares (3SLS) method. The empirical results indicate that the spread of ICTs, particularly mobile phones and the internet, significantly promotes the development of financial inclusion, which serves as an effective transmission channel for the impact of ICTs on economic growth. Conversely, fixed-line telephony has no significant effect on financial inclusion. To maximize the impact of ICTs on the development of the financial sector in support of economic growth, WAEMU countries must prioritize investment in digital infrastructure, especially in rural and peri-urban areas. It is also essential to foster the growth of fintech companies and mobile financial services, ensuring transaction security, data protection, and system interoperability.

Fragmentation of electronic health records in oncology hinders coordinated care, delays diagnoses, and limits therapeutic personalization. Blockchains promise to promote secure, interoperable, and patient-centered data governance; however, patient perceptions of blockchains remain underexplored, particularly in middle-income countries such as Brazil. We assessed opinions, attitudes, and willingness among patients with cancer to digitally share clinical information and the feasibility of applying blockchains to restructuring secure health data sharing in the Brazilian public health context. We had three research questions: (1) What is the level of digital health tool acceptance among patients with cancer in Brazil? (2) Which sociodemographic factors are associated with willingness to share health data? (3) Are blockchains feasible and acceptable for restructuring secure oncology data sharing? An exploratory, descriptive, cross-sectional self-report survey was conducted at Hospital Santa Izabel, a national oncology reference center in Salvador, Bahia, Brazil, between September and November 2023. A convenience sample of 110 outpatients with cancer was recruited systematically; data were collected via a self-administered questionnaire. The 20-item instrument, developed de novo and validated via expert panel and pilot testing, covered 5 content domains yielding 3 composite scoring domains: self-management, adherence, and governance. We used Cronbach α to assess internal consistency, independent 2-tailed t tests, 1-way ANOVA, and Pearson correlations to compare domain scores across sociodemographic groups, and a chi-square goodness-of-fit test to examine trust proportions across recipient types. We received sufficiently complete responses from 94.5% (104/110) of patients. The sample was predominantly female (63/98, 64.3%), self-identified as pardo (mixed-race; 64/98, 65.3%), and lower income (55/96, 57.3% earned less than twice the minimum wage). Acceptance of technology was high: 86.4% (95/110) would use health apps and 89.1% (98/110) expressed interest in prevention-focused applications. Trust in data sharing varied significantly across recipient types (χ23=210.4; P<.001): 79.1% (87/110) trusted health care professionals, 51.8% (57/110) hospitals, 15.5% (17/110) the pharmaceutical industry, and 10% (11/110) the government. Anonymization and encryption significantly increased willingness to share (92/110, 83.6%). Younger patients (18-59 years) showed significantly higher adherence scores than those aged ≥60 years (mean 76.49, SD 19.16 vs mean 65.83, SD 26.66; t95=2.29; P=.02). Domain reliability was good to excellent (Cronbach α=0.8807 [adherence], 0.8504 [self-management], and 0.7576 [governance]). Patients with cancer in Brazil demonstrated high acceptance of digital health tools and openness to data sharing when privacy, security, and governance are guaranteed. This supports the feasibility of blockchain-based health data management systems, provided they incorporate patient-centered principles, digital inclusion strategies, and robust governance aligned with Brazilian regulations (the General Data Protection Law) and the Unified Health System (Sistema Único de Saúde) infrastructure. Importantly, patient support reflected acceptance of blockchain's functional principles, data security, anonymization, and auditability rather than familiarity with the technology itself, a distinction with direct implications for future implementation studies.

Digital transformation is fundamentally changing the diagnosis, monitoring and treatment of multiple sclerosis. The integration of multimodal data from imaging, laboratory tests, clinical assessments, patient-reported outcomes and continuous measurements via wearables is creating high-resolution, longitudinal profiles of disease progression. Based on this data, modern analysis methods and artificial intelligence enable predictive models for disease activity, progression and therapeutic response, supporting personalised decision-making. Digital patient pathways and patient portals open up new options for participatory, standardised care, while telemedicine, telerehabilitation and digital health applications complement care regardless of location and time. In research, real-world data, federated learning and virtual, decentralised studies are accelerating patient-centred evidence generation. Concepts such as the digital twin outline the next stage of development in simulation-based precision medicine. Key challenges relate to data protection and data security, data quality, interoperability, bias, transparency and the traceability of algorithmic decisions. Overall, digitalisation offers substantial opportunities to detect disease activity earlier, optimise treatment goals and improve quality of life and care - provided that technical, regulatory and ethical requirements are consistently addressed and translated into scalable care models.

Voluntary horizontal sharing of confidential food safety data among companies for joint analysis can improve food safety, efficiency, and decision-making, especially for rare events. Despite its potential benefits, horizontal data sharing in the food industry has lagged, with limited research exploring the reasons for hesitation. To address this gap, we conducted in-depth semi-structured interviews with 27 food industry leaders. Four themes emerged: (1) benefits of data sharing, (2) technical barriers, (3) trust as a determinant of data sharing decisions, and (4) data governance as a solution. Across these themes, we found that companies face trade-offs when deciding to share food safety data, weighing risks against benefits. Data sharing decisions were strongly influenced by trust in stakeholders (e.g., industry peers, regulatory bodies, customers) and in data protection measures. Data governance emerged as a solution to concerns stemming from trust, such as loss of control once data is shared. Taken together, the findings reveal underlying tensions between individual firm incentives and collective benefits, including uneven cost-benefit distributions, opportunism concerns, and participation cost asymmetries. These findings offer timely insights to guide data sharing initiatives and prioritize areas for future research.

The increasing documentation burden in electronic health records makes it difficult to obtain arapid overview of relevant prior information in complex disease courses. Especially in highly digitized settings such as radiology and interdisciplinary case conferences, large volumes of heterogeneous documents must be reviewed under time pressure. The aim was to develop alarge language model (LLM)-based patient synopsis to accelerate information retrieval while ensuring transparency, physician oversight, and data protection. At the University Medical Center Freiburg, asystem was developed for the automated integration of clinical documents from multiple primary systems, including the electronic health record (EHR), picture archiving and communication system (PACS), and other subsystems. It uses retrieval-augmented generation (RAG), metadata harmonization, vector search, and agentic retrieval for the context-sensitive selection of relevant content. The system operates with role-based access control, end-to-end source attribution, and processing in asovereign cloud without persistent data storage. The system enables patient-specific queries, structured longitudinal summaries, and automated, source-grounded summaries to support preparation for interdisciplinary case discussions. All statements remain traceable to primary documents, while medical interpretation and decision-making remain the responsibility of the treating physician. Interoperable information systems and adata protection framework in accordance with the General Data Protection Regulation (GDPR), the German Social Code BookV (SGBV), and the EU Artificial Intelligence Act are prerequisites for implementation. LLM-based patient synopses can support text-intensive clinical workflows, provided that controlled retrieval, clear source attribution, physician validation, and an interoperable, regulation-compliant system architecture are in place. Prospective evaluations are required before routine clinical use.

In wireless communication, the multipath effect and the time-varying channel due to mobility will directly lead to the key update cycle lagging far behind the channel change, which is difficult to effectively resist various malicious attacks and stealing behaviors, and affects the effect of privacy data protection in wireless communication. To this end, a deep learning-based dual chaos encryption method is proposed for wireless communication privacy data. Combining the chaotic characteristics of one-dimensional Logistic mapping and two-dimensional Henon mapping, the dual chaotic key is generated to extend the key space and improve the anti-attack ability; and the bidirectional long and short-term memory network (BiLSTM) is used to analyze the data such as key usage records, accurately predict the timing of the key updating, and generate a new key when anomalies are detected, and then distribute it securely. Taking the updated double chaotic key as input, the AES algorithm is used to realize wireless communication privacy data encryption through key expansion, initial round encryption, multiple rounds of iterative encryption and final round encryption, while the decryption process restores the plaintext by inverse operation. Experiments demonstrate that the method can effectively realize wireless communication privacy data encryption, and the security index can reach more than 0.94 in the face of different types of network attacks. It demonstrates that the proposed method can have the ability to resist all kinds of attacks and protect the security of private data.