Data Security Research Articles (Page 1)

Abstract Polarization control plasmonic nanostructures provide a unique route to manipulate light–matter interactions at the nanoscale and are particularly powerful for information security applications, where polarization-encoded color images can be used for optical encryption and anticounterfeiting. Conventional plasmonic materials such as Au and Ag, however, suffer from poor thermal stability, limiting their integration into robust, CMOS-compatible devices. Here, we present a polarization-encoded color image platform based on refractory HfN plasmonic metasurfaces, which combine gold-like optical properties with exceptional hardness, compositional tunability, and superior high-temperature resilience. Periodically patterned HfN nanoantennas with widths of 200 nm exhibit well-defined localized surface plasmon resonances in the visible spectrum (628 and 564 nm) and can be selectively excited by orthogonal linear polarizations. We designed and realized a polarization-encoded color image in which distinct color channels are revealed under x- and y-polarized illumination, enabling decryption of hidden information. Under unpolarized illumination, the superposition of color channels effectively conceals the message, achieving robust optical encryption. Our results establish HfN plasmonic nanostructures as a key material platform for next-generation nanophotonics, uniquely combining gold-like optical properties with exceptional thermal robustness. Even after high-temperature annealing, HfN retains its plasmonic response, enabling reliable polarization-resolved color image encoding and decryption. This breakthrough paves the way for thermally resilient metasurfaces for secure data encryption, anticounterfeiting, and robust operation in extreme environments.

This study thoroughly examines Generation Z's perceptions of the use of artificial intelligence (AI) in digital marketing among MSMEs in Bali. The evolution of digital technology, particularly AI, has revolutionized marketing strategies by enabling automation, personalized ads, and real-time analysis of consumer behavior, thereby helping MSMEs expand their markets and enhance operational efficiency. However, the primary challenges in AI implementation are the limited digital literacy among MSMEs and concerns about consumer data privacy. As the dominant digital consumer in Indonesia, Generation Z strongly favors personalized, efficient shopping experiences. Yet, they also exhibit a high level of scrutiny towards the transparency and security of the personal data used in digital marketing. This study employs a qualitative approach that integrates in-depth interviews and participant observation with Gen Z in the Badung and Denpasar regions. Through this method, the study thoroughly investigates how Gen Z reacts to AI-based advertising and marketing strategies, including their attitudes towards privacy and ad frequency. The anticipated results are expected to provide comprehensive, insightful recommendations for MSMEs and policymakers to establish an inclusive, competitive digital marketing ecosystem that safeguards the rights of young consumers in the ever-evolving digital era. This study also aspires to enhance comprehension of the significance of digital literacy and the ethical use of AI in MSME marketing in Bali.

Abstract In the context of rapid digital transformation and the burgeoning field of big data, the management of data providers has emerged as a critical element, heavily dependent on robust regulatory frameworks. This paradigm shift warrants a comprehensive investigation within the domain of public management. Based on the policy review, literature analysis, and conceptual clarification, this study utilizes CiteSpace for a quantitative bibliometric analysis to discern key research hotspots, emerging trends, and the historical development of the field. The findings indicate an ascending trajectory in data provider research, with significant contributions from both international and domestic scholars. Nevertheless, there exists an urgent need for enhanced collaboration across teams and institutions. Predominant countries such as the USA, Australia, and China underscore the global and collaborative nature of this research. Central themes addressed include data production, cloud computing, and data security, each varying regionally in emphasis. A notable transition is observed from foundational and academic research to practical applications and advanced technologies. Moving forward, to standardize data markets, academia should establish theoretical frameworks, integrate security governance, and refine evaluation systems. Governments should enhance legal oversight, foster cross-sector collaboration, and accelerate ecosystem development for public management modernization. Data vendors require end-to-end security compliance, technical standardization, and market-driven innovation to maintain competitive sustainability. These insights provide both theoretical and practical guidance, promoting comprehensive research and sustainable evolution in this dynamic sector.

Secure and efficient data sharing in Industrial Internet of Things (IIoT) is a continuous difficulty due to the limits of static proxy node selection, centralized designs, and the lack of agility in dynamic situations. Traditional systems often suffer from excessive latency, single points of failure, tight access control, and vulnerability to targeted attacks. To address these limitations, we offer BDEQ (Blockchain-based Dynamic Edge Q-learning), a novel framework combining blockchain smart contracts and deep Q-learning for real-time, trust-aware proxy node selection. Unlike static systems, BDEQ's reinforcement learning agent dynamically selects appropriate edge nodes based on performance, resource availability, and trust criteria. This ensures secure access control, decentralized auditing, and resilience to security attacks. In a simulated gas-industry IIoT context, BDEQ lowered data access latency by 35% and boosted throughput by 28% over baseline approaches while giving greater resilience to attacks. These results validate BDEQ's relevance to next-generation IIoT contexts needing adaptive, decentralized, and secure data sharing.

The operating room (OR) is a data-rich environment and largely follows closed-door policies for health data security and privacy. To overcome this, the authors have developed a unique sensor-driven, secure, cloud-based scalable data framework enabling real-time acquisition, streaming, and analytics of OR data, accessible to surgeons as feedback and performance reporting. For system validation, this dynamic digital platform was deployed across neurosurgical centers for precise, accurate, and fast analytics of surgical data, establishing an Internet of Things-OR (IoT-OR). Through recent deployment of a novel sensorized surgical device called the SmartForceps System, the authors established and validated a data-driven interconnected platform for neurosurgery, the IoT-OR. The system includes sensorized surgical bipolar forceps, allowing quantification of tool-tissue force in real time. Surgical microscope video live-streamed into the software allows a videographic data display time-stamped to tool-tissue interaction, enabling both quantification of surgery and real-time interrogation for feedback and guidance. This IoT platform, with secure data containers by each surgical center and hosted in the cloud, allows data flow and automated analytics through its custom artificial intelligence (AI) model, enriching the model with each new case in perpetuity. The output is a surgeon performance report unique to each procedure and accessible by the surgeon via secure personalized devices and authentication. In more than 250 neurosurgical procedures, spanning 3 neurosurgical units across western Canada (University Alberta Hospital, Edmonton, Alberta; Vancouver General Hospital, Vancouver, British Columbia; and Foothills Medical Centre, Calgary, Alberta, Canada), the system successfully demonstrated that a cloud-driven end-to-end secure platform for surgical procedures can be enabled and operated in real time. Linked to a smart surgical device, built-in intelligent software interface with cloud connectivity, a unique IoT-OR platform has thus been established, with built-in security and scalability to include other data sources (e.g., OR equipment, electronic medical records), multiple centers, and surgeons globally. The study thus demonstrates the utility of sensors, AI, and cloud interconnectivity in real-time monitoring, analytics, and feedback as a digital footprint of surgery. Using and quantifying closed-door OR data and weaving them into a secure and innovative data-rich pipeline, the system offers a glimpse toward standardization of surgery at the level where the tool meets the tissue.

Digital conversational agents (or "chatbots") that can generate human-like conversations have recently been adapted as a means of administering mental health interventions. However, their development for youth seeking mental health services requires further investigation. This youth-engaged scoping review synthesizes the recent research on digital conversational agents for youth seeking mental health or substance use services. Studies were included if they were published between 2016 and 2025 and examined digital conversational agents for youth aged 11 to 24 years with mental health or substance use challenges in clinical settings. Systematic literature searches were conducted in February 2024 in multiple databases and updated in March 2025. Data were extracted using codeveloped forms and synthesized narratively. Ten studies were included, all focusing on mental health. Seven examined the acceptability and feasibility of digital conversational agents; others explored youth perceptions of use, design, and content, with some exploration of impact on mental health symptoms. Eight of ten studies reported high acceptability or positive user experiences. Three were randomized controlled trials that found potential reductions in depressive symptoms. Reporting on the ethical standards was limited. No studies focused on substance use alone. Literature on digital conversational agents for treatment-seeking youth is emerging but limited. Future rigorous research is needed that prioritizes data security, safety measures, and youth co-design in the development of safe, engaging, digital conversational agents for youth with mental health conditions.

The Norwegian Health Archives Registry (NHAR) is a national initiative dedicated to digitising, centralising, and providing access to historical full-text patient health records (PHRs) for research purposes. Established in 2019, NHAR includes PHRs from the deceased population in Norway's specialist healthcare services, offering a unique long-term data source for future research. NHAR has now digitised 1.7 million paper-based PHRs, covering medical history dating back to 1875. The registry is now expanding to include digital-born PHRs. This article describes NHAR's innovation potential as a health registry, its data management processes, and the integration of artificial intelligence (AI) tools to facilitate data management and research in compliance with strict health data regulations. NHAR's data value chain includes structured metadata acquisition, large-scale digitisation and secure data delivery for research. The workflow includes a custom optical character recognition (OCR) tool tailored to Norwegian medical terminology, concept-based search tools for unstructured clinical full text and robust strategies for long-term data management. A novel AI-based de-identification system automatically detects and masks personal identifiers in digitised PHRs. Despite these innovations, challenges persist in processing handwritten and historical PHRs due to OCR limitations and language-specific complexities. Key challenges include improving data quality, enhancing OCR accuracy and refining AI tools for information retrieval, data extraction and de-identification. NHAR offers significant potential for interdisciplinary research across various medical fields.Implications for health information management practice:NHAR establishes a foundation for secure access to historical health data and introduces advanced data management strategies to facilitate future research.

This project, titled “A Web-Based Data Driven Analytics System for Income and Operations Management Using Linear Regression for Modern Concept Prints,” was developed to address the inefficiencies of manual income tracking and operations management in small enterprises. Income management, which includes tracking revenue, managing expenses, and ensuring profitability, is crucial for financial planning and long-term sustainability. Operations management, which involves the efficient handling of day-to-day processes such as task delegation, inventory monitoring, and order fulfillment, is equally essential for business optimization. When these areas are not integrated or managed manually, it becomes difficult to control costs, optimize performance, and make informed decisions. This study follows an applied research approach, focusing on the development of a Web-Based System to solve real-world business challenges faced by Modern Concept Prints, a local printing business. The business had been relying on spreadsheets and verbal coordination for its operations, leading to frequent delays, inaccurate records, and limited forecasting capabilities. To address these issues, the researchers designed and implemented a centralized platform that automates key business processes, including Sales Monitoring, Inventory Management, and Task Tracking, while utilizing Linear Regression for Income Prediction based on historical data. The system also integrates Predictive Analytics to forecast future income, thus enhancing decision-making. The project followed the Spiral Model as its software development methodology, allowing for iterative development, continuous risk assessment, and frequent refinement of the system based on user feedback. Developed using PHP, MySQL, HTML, CSS, and JavaScript, the system offers a dynamic, user-friendly interface that supports real-time data analysis and visualization. Evaluation results, guided by ISO 25010 quality standards, showed high satisfaction among both technical and user respondents in terms of System Usability, Data Security, functionality, reliability, and security. The system significantly improved operational workflows, reduced manual errors, and enhanced financial planning through automated income prediction and sales monitoring. The project demonstrates how integrating automation, Predictive Analytics, Linear Regression, and business management can help small businesses optimize decision-making, productivity, and long-term sustainability. Future recommendations include adding accounting and payroll modules, mobile compatibility, and advanced forecasting algorithms to further enhance scalability and performance.

Abstract - The fast growth of the Internet of Things (IoT) and Industry 4.0 technologies has greatly changed the manufacturing industry, making smart manufacturing possible. Industry 4.0 refers to the use of advanced tools such as IoT, artificial intelligence (AI), cloud platforms, and big data analytics to build a highly connected and automated production environment. This paper explores the current progress of IoT-based smart manufacturing, outlining its challenges, opportunities, and expected future developments. It reviews the main components that support Industry 4.0, including IoT sensors and devices, cloud computing, data analytics, and cyber security systems. The study also discusses the major benefits of Industry 4.0, such as improved efficiency, better product quality, cost reduction, and lower levels of waste. This shift from traditional manufacturing to modern automated systems is driven by the integration of technologies like AI, cyber-physical systems, and real-time data processing. However, the transition also introduces several challenges, including issues with integrating different technologies, concerns about data privacy and security, and the need for workforce training and skill development. Overall, the paper highlights how Industry 4.0 can support real-time monitoring, predictive capabilities, and smarter decision-making in manufacturing environments.

Abstract—The Internet of Medical Things (IoMT) is rapidly transforming healthcare by enabling real-time monitoring, re- mote diagnosis, and intelligent decision-making. While these technologies improve patient care and efficiency, they also in- troduce new vulnerabilities in terms of data security, patient privacy, and system reliability. The growing reliance on inter- connected medical devices makes IoMT systems an attractive target for adversaries, with risks ranging from data breaches and adversarial manipulation to system-wide intrusions. Traditional security frameworks, such as centralized intrusion detection systems or rule-based approaches, struggle to keep up with the evolving nature of threats and the unique constraints of IoMT environments, including limited device resources, latency sensitivity, and the need for privacy preservation. To overcome these limitations, we present an integrated framework that combines federated learning, blockchain, and advanced deep learning models to provide a holistic solution for secure data processing and intrusion detection in IoMT ecosystems. The pro- posed architecture introduces quantum-based authentication for stronger device-level security, privacy-preserving collaborative training to enable distributed model learning without exposing raw patient data, and noise-driven feature masking to minimize the risks of adversarial attacks and poisoning attempts. In ad- dition, the framework reduces communication overhead through prototype-driven representation learning and optimization-aware aggregation, ensuring efficiency even in bandwidth-constrained medical networks. Index Terms—IoMT, Federated Learning, Blockchain, Deep Learning, Privacy Preservation, Intrusion Detection

The article examines the impact of digital proficiency on service delivery in private sector hospitals in Harare, Zimbabwe. It investigates how healthcare professionals’ competence in using digital tools and technologies influences the effectiveness and transformation of healthcare services. Using a qualitative approach, data were collected through interviews with 32 participants, including healthcare professionals, administrative staff and patients. The study identified key competencies in digital literacy, such as the use of electronic health records (EHRs), digital appointment systems and telemedicine platforms and assessed their influence on operational effectiveness. Metrics used to evaluate service outcomes included reduced patient wait times; lower administrative error rates and higher patient satisfaction scores. Results reveal that higher levels of digital proficiency among hospital staff are associated with streamlined administrative processes, improved accuracy in patient data handling and enhanced communication with patients. Patients in digitally proficient hospitals reported greater satisfaction, citing efficient appointment scheduling and easier access to medical information as primary benefits. However, the study also uncovers several gaps. Limited training opportunities are noted, particularly in the use of advanced hospital management systems, data security protocols and telehealth platforms. Furthermore, resistance to technology adoption emerged as a significant barrier, with contributing factors including lack of understanding, fear of job displacement and generational differences in technology use. The study concludes with recommendations for targeted and continuous professional development programmes focused on bridging identified skill gaps. It also suggests implementing change management strategies to address resistance, including peer mentoring, inclusive training sessions and clear communication of the benefits of digital tools in enhancing care deliver.

The transformative potential of big data across various industries has been demonstrated. However, the data held by different stakeholders often lack interoperability, resulting in isolated data silos that limit the overall value. Collaborative data efforts can enhance the total value beyond the sum of individual parts. Thus, big data sharing is crucial for transitioning from isolated data silos to integrated data ecosystems, thereby maximizing the value of big data. Despite its potential, big data sharing faces numerous challenges, including data heterogeneity, the absence of pricing models, and concerns about data security. A substantial body of research has been dedicated to addressing these issues. This paper offers the first comprehensive survey that formally defines and delves into the technical details of big data sharing. Initially, we formally define big data sharing as the act of data sharers to share big data so that the sharees can find, access, and use it in the agreed ways and differentiate it from related concepts such as open data, data exchange, and big data trading. We clarify the general procedures, benefits, requirements, and applications associated with big data sharing. Subsequently, we examine existing big data-sharing platforms, categorizing them into data-hosting centers, data aggregation centers, and decentralized solutions. We then identify the challenges in developing big data-sharing solutions and provide explanations of the existing approaches to these challenges. Finally, the survey concludes with a discussion on future research directions. This survey presents the latest developments and research in the field of big data sharing and aims to inspire further scholarly inquiry.

In the service sector, accounting information systems face growing risks in data security, unauthorized access, and fraud. Strong internal control and risk management are essential for efficiency and trust. This study proposes an intelligent accounting information system with four modules: (1) data collection for financial and operational data completeness; (2) information encryption using Advanced Encryption Standard with 256-bit key in Galois/Counter Mode (AES-256-GCM), Rivest-Shamir-Adleman (RSA), and Elliptic Curve Digital Signature Algorithm (ECDSA); (3) real-time risk assessment based on probability and impact; and (4) decision support comparing linear regression, support vector machines, and artificial neural networks for cost prediction. Results show the artificial neural network achieves the highest accuracy and is adopted for cost optimization and budgeting. The system enhances security, enables proactive risk management, and supports data-driven decisions.

Because of the rapid acceleration of cloud computing, data transfer security and intrusion detection in cloud networks have become emerging areas of concern. All traditional security mechanisms have central vulnerabilities, cannot detect real-time threats, and are ineffective against zero-day attacks. Signature-based approaches of existing intrusion detection systems (IDS) do not cover the dynamically changing nature of cyber threats. Conventional blockchain security methods suffer from poor scalability and dynamic threat analysis. Therefore, this research proposes integrating Ethereum Blockchain and Deep Learning to construct a well-founded security framework for cloud networks with data migration security and real-time intrusion detection. The architecture has five distinct methods, each of which deals with particular security issues. Blockchain-Aware Federated Learning for Secure Model Training (BAFL SMT) guarantees tamper-proof and decentralized deep learning model training, which reduces model poisoning attacks by 98.4%. Graph Neural Networks for Adaptive Intrusion Detection (GNN-AID) captures graph structures for real-time anomaly detection in networks while reducing false positives to 1.2%. Quantum-inspired Variational Autoencoders (QI VAE ZDAD) provide enhanced zero-day attack detection, with an improved detection rate of 92%. Self-Supervised Contrastive Learning for Blockchain Security Auditing (SSCL-BSA) detects smart contract vulnerabilities automatically, resulting in an 87% reduction in fraud risk. Finally, Hierarchical Transformers for Secure Data Migration (HT SDM) enhance the transfer security of large-scale cloud data, achieving an attack classification accuracy of 99.1%. Overall, this multi-layer security framework will greatly enhance cloud security by preserving data integrity, cutting down the intrusion detection time by up to 65%, and enhancing response mechanisms. By marrying the immutable transparency of blockchain with superior anomaly detection at deep learning, this research provides a scalable, real-time, and intelligent approach to strengthening security against the backed-up transfer of data within cloud networks.

Architecting Cloud-Based CPQ Solutions for Healthcare Enterprise Transformation: A Framework for Medical Device Sales and Compliance

Federated Learning (FL) has emerged as a promising framework for multi-institutional medical artificial intelligence, enabling collaborative model development while preserving data privacy and security. Despite increasing research on federated approaches for cardiovascular disease prediction, previous reviews have largely focused on disease-specific perspectives without systematically comparing data modalities. This study comprehensively examines 28 representative investigations from the past five years, including 17 biosignal-based and 11 electronic health record (EHR)-based applications. Biosignal-based FL emphasizes personalized electrocardiogram (ECG) classification, mitigation of non-independent and identically distributed (Non-IID) data, and Internet of Things (IoT)-based monitoring using methods such as client clustering, asynchronous learning, and Bayesian inference. In contrast, EHR-based studies prioritize large-scale hospital collaboration, adaptive optimization, and secure aggregation through distributed frameworks. By systematically comparing methodological strategies, performance trade-offs, and clinical feasibility, this review highlights the complementary strengths of biosignal- and EHR-based approaches. Biosignal frameworks show strong potential for personalized, low-latency cardiac monitoring, whereas EHR frameworks excel in scalable and privacy-preserving decision support. Building upon the limitations of earlier reviews, this paper introduces data-type-centric design guidelines to enhance the reliability, interpretability, and clinical scalability of FL in cardiovascular diagnosis and prediction.

This work explores the potential of connected, digitalized Wire Arc Additive Manufacturing (WAAM) within the framework of Industrie 4.0, analyzing it through distinct process layers: workpiece, assembly, and product. Each layer presents unique timeframes and stakeholder interactions, necessitating varied data infrastructure demands, including a consideration of data security and privacy challenges. The workpiece layer mostly covers the local production setup and is thus directly coupled with the product and process quality as well as maintaining a safe operation. In the assembly layer, ensuring interoperability among diverse stakeholders is crucial, requiring clear definitions of responsibilities and access rights to enhance data exchange. The product layer prioritizes the reliability and trustworthiness of information for informed decision-making, advocating for solutions that guarantee authenticity and verifiability while addressing privacy concerns through techniques like privacy-preserving computing. The paper identifies a critical gap in real-world applications of these concepts in additive manufacturing. It proposes a data-driven quality control approach to enhance process and product quality in arc welding, leveraging digital shadows to create effective interfaces within production networks. This approach has demonstrated potential reductions in welding fume emissions by 12-40%, alongside connected applications that minimize exposure and energy consumption.

The open, shared, and virtualized nature of the cloud also introduces numerous security threats, which posing significant challenges to big data security in enterprises. Through case studies and comparative methods, this paper systematically identifies core security issues related to big data in cloud environments, including data breaches, unauthorized access, cloud platform vulnerabilities, and cross-border data compliance dilemmas. This paper constructs a tripartite integrated security framework encompassing technology, management, and compliance: at the technical level, end-to-end encryption, multi-factor authentication, and zero-trust architecture are adopted; at the management level, a full lifecycle security management mechanism is established, incorporating the principle of least privilege, regular assessments, disaster recovery, and training; at the compliance level, adherence to relevant domestic and international regulations is emphasized, with clear requirements for data sovereignty and cross-border data transfer. The research recommends that enterprises develop a security governance framework integrating "defense-in-depth, continuous optimization, and shared responsibility." By strengthening technical measures, optimizing management practices, and ensuring regulatory compliance, enterprises can comprehensively enhance the confidentiality, integrity, and availability of data in cloud environments, thereby providing a secure and reliable foundation for their digital transformation.

Artificial intelligence (AI) is a rapidly evolving technology with the potential to revolutionize the health care industry. In Saudi Arabia, the health care sector has adopted AI technologies over the past decade to enhance service efficiency and quality, aligning with the country's technological thrust under the Saudi Vision 2030 program. This review aims to systematically examine the impact of AI on health care quality in Saudi Arabian hospitals. A meticulous and comprehensive systematic literature review was undertaken to identify studies investigating AI's impact on health care in Saudi Arabia. We collected several studies from selected databases, including PubMed, Google Scholar, and Saudi Digital Library. The search terms used were "Artificial Intelligence," "health care," "health care quality," "AI in Saudi Arabia," "AI in health care," and "health care providers." The review focused on studies published in the past 10 years, ensuring the inclusion of the most recent and relevant research on the effects of AI on Saudi Arabian health care organizations. The review included quantitative and qualitative analyses, providing a robust and comprehensive understanding of the topic. A systematic review of 12 studies explored AI's influence on health care services in Saudi Arabia, highlighting notable advancements in diagnostic accuracy, patient management, and operational efficiency. AI-driven models demonstrate high precision in disease prediction and early diagnosis, while machine learning optimizes telehealth, electronic health record compliance, and workflow efficiency, despite adoption challenges like connectivity limitations. Additionally, AI strengthens data security, reduces costs, and facilitates personalized treatment, ultimately enhancing health care delivery. The review underscores that AI technologies have significantly improved diagnostic accuracy, patient management, and operational efficiency in Saudi Arabia's health care system. However, challenges such as data privacy, algorithmic bias, and robust regulations require attention to ensure successful AI integration in health care.

The rise of digital financial services such as Shopee PayLater has gained increasing popularity among university students due to the convenience and flexibility it offers in transactions. However, this ease of access also brings forth a range of consumer protection issues, including lack of transparency in information, potential misuse of personal data, and low consumer awareness of their rights. This study aims to analyze the implementation and influencing factors of consumer protection for Shopee PayLater users among students at Universitas Negeri Padang. A descriptive qualitative approach was employed, with primary data obtained through interviews with student users and lecturers knowledgeable about consumer protection issues, and secondary data sourced from regulations and official documents. Data were analyzed using John F. Kennedy’s theory of basic consumer rights, which includes the right to safety, the right to be informed, the right to choose, and the right to be heard. The findings reveal that the implementation of consumer protection in this context remains suboptimal. Data security systems lack transparency, service information is difficult to access due to complex terms and conditions, consumer choices are often influenced by aggressive promotions, and complaint mechanisms are not adequately responsive. Internal factors affecting consumer protection include low financial literacy and consumerist behavior among students, while external factors involve weak regulation, limited government oversight, and a lack of corporate social responsibility among digital service providers. These findings underscore the importance of synergy between government regulation, ethical business practices, and consumer legal awareness to foster fair and balanced digital transactions.