Data Privacy Research Articles

Particular qualities of evaluation of electronic card data in modern healthcare

This article is devoted to the study and analysis of electronic health records (EHR), which are the main tool in the field of digital healthcare. EHR provide faster and more efficient transfer of medical information between various institutions and specialists, which contributes to improving the quality of medical care. The article discusses various aspects of the implementation and use of EHR, including technical difficulties, data privacy and security issues. Special attention is paid to the influence of EHR on the processes of diagnosis and treatment, as well as on the interaction between patients and medical professionals. Based on a review of current research and practical examples, the authors propose recommendations for optimizing the use of EHR in medical institutions. In conclusion, the prospects for further development of EHR technologies and their role in the transformation of modern medicine are discussed.

Contrastive Dual-Pool Feature Adaption for Domain Incremental Remote Sensing Scene Classification

Remote sensing image classification has achieved remarkable success in environmental monitoring and urban planning using deep neural networks (DNNs). However, the performance of these models is significantly impacted by domain shifts due to seasonal changes, varying atmospheric conditions, and different geographical locations. Existing solutions, including rehearsal-based and prompt-based methods, face limitations such as data privacy concerns, high computational overhead, and unreliable feature embeddings due to domain gaps. To address these challenges, we propose DACL (dual-pool architecture with contrastive learning), a novel framework for domain incremental learning in remote sensing image classification. DACL introduces three key components: (1) a dual-pool architecture comprising a prompt pool for domain-specific tokens and an adapter pool for feature adaptation, enabling efficient domain-specific feature extraction; (2) a dual loss mechanism that combines image-attracting loss and text-separating loss to enhance intra-domain feature discrimination while maintaining clear class boundaries; and (3) a K-means-based domain selector that efficiently matches unknown domain features with existing domain representations using cosine similarity. Our approach eliminates the need for storing historical data while maintaining minimal computational overhead. Extensive experiments on six widely used datasets demonstrate that DACL consistently outperforms state-of-the-art methods in domain incremental learning for remote sensing image classification scenarios, achieving an average accuracy improvement of 4.07% over the best baseline method.

Methodological and Technological Advancements in E-Learning

The present survey examines the intersection of methodological advancements and technological innovations in e-learning, emphasizing their transformative impact on modern education. It systematically explores instructional design frameworks, adaptive learning systems, immersive technologies, and data-driven analytics, highlighting their role in fostering personalized, scalable, and inclusive learning environments. Through the integration of pedagogical theories with advanced tools like artificial intelligence (AI), augmented reality (AR), virtual reality (VR), and mixed reality (MR), this study demonstrates how e-learning systems enhance engagement, retention, and accessibility. The survey addresses critical challenges such as the digital divide, data privacy, and resistance to adoption, offering evidence-based strategies to mitigate these issues. It underscores the importance of bridging equity gaps while maintaining scalability and sustainability, particularly in underserved regions. By synthesizing state-of-the-art research and practical applications, this work provides actionable insights into the future of e-learning, advocating for a balanced approach to innovation that aligns technological capabilities with the diverse needs of global learners. The findings contribute to the broader discourse on sustainable, inclusive, and effective digital education ecosystems.

A Randomized Response Framework to Achieve Differential Privacy in Medical Data

In recent years, differential privacy has gained substantial traction in the medical domain, where the need to balance privacy preservation with data utility is paramount. As medical data increasingly relies on cloud platforms and distributed sharing among multiple stakeholders, such as healthcare providers, researchers, and policymakers, the importance of privacy-preserving techniques has become more pronounced. Trends in the field focus on designing efficient algorithms tailored to high-dimensional medical datasets, incorporating privacy guarantees into federated learning for distributed medical devices, and addressing challenges posed by adversarial attacks. Our work lays a foundation for these emerging applications by emphasizing the role of randomized response within the broader differential privacy framework, paving the way for advancements in secure medical data sharing and analysis. In this paper, we analyze the classical concept of a randomized response and investigate how it relates to the fundamental concept of differential privacy. Our approach is both mathematical and algorithmic in nature, and our purpose is twofold. On the one hand, we provide a formal and precise definition of differential privacy within a natural and convenient probabilistic—statistical framework. On the other hand, we position a randomized response as a special yet significant instance of differential privacy, demonstrating its utility in preserving individual privacy in sensitive data scenarios. To substantiate our findings, we include key theoretical proofs and provide indicative simulations, accompanied by open-access code to facilitate reproducibility and further exploration.

Advancing Emotion Recognition in Wearable Devices: Core Technologies, Challenges and Practical Solutions

This paper reviews the advancements and challenges in emotion recognition systems, focusing on their applications in wearable devices. It examines core technologiesmultimodal data fusion, deep learning models, and personalized frameworksin addressing critical issues such as data quality, computational efficiency, and privacy protection. By highlighting innovative solutions, including edge computing, lightweight architectures, and advanced privacy-preserving techniques, the paper emphasizes their role in enhancing the robustness, scalability, and security. By integrating real-world applications with state-of-the-art methodologies, this review underscores the transformative potential of wearable emotion recognition technologies for enhancing well-being and daily life. This review also examines current challenges in emotion recognition and provides potential solutions to address these issues

Advancing Genetic Engineering through AI: Sequencing and Editing Innovations

As an important part of modern biotechnology, genetic engineering is widely used in fields such as disease treatment, agricultural improvement, and environmental protection. Gene sequencing technology, especially next-generation sequencing technology, provides a powerful tool for studying biological genetic information. However, with the rapid growth of genomic data, how to efficiently and accurately analyze and apply this huge data has become a major challenge facing genetic engineering. In recent years, artificial intelligence (AI) technology, especially deep learning, has been widely used in the automated processing of large-scale data and has shown great potential in genetic engineering. AI technology not only shows advantages in gene editing optimization, genetic variation detection and genome association analysis, but also significantly improves the efficiency and accuracy of genetic data analysis. Although AI has brought many conveniences in genetic engineering, challenges such as technology transparency, data quality issues, and ethics and privacy protection still need to be solved. This article explores the application of artificial intelligence in genetic engineering sequencing and data analysis, analyzes how AI can improve the efficiency and accuracy of genetic data analysis, and discusses the potential contribution of AI in gene editing and precision medicine. As AI continues to develop, it is expected to play an increasingly important role in fields such as genomics, gene editing, and precision medicine, and provide more effective strategies for future disease treatment

Application of Artificial Intelligence in Healthcare: Trends, Challenges in Disease Diagnosis and Management

This study thoroughly reviews the status of applications of artificial intelligence (AI) in healthcare, trends regarding AI usage for different disease types and problems that hamper their further progress. The study used a literature review and data analysis by locating relevant current articles on AI in healthcare through the PubMed database. The work analyzes AI use in cancer, cardiovascular diseases and neurological disorders as well as the bottlenecks in the real-world deployment of healthcare. The findings of the study indicate that, while AI has demonstrated the potential to improve diagnostic precision, several obstacles persist in relation to data privacy, ethical considerations, and model interpretability. In conclusion, this review offers an assessment of the current state of AI applications in healthcare and identifies key areas of concern that necessitate further investigation. By addressing these challenges, future innovations can be more effectively developed and broadly implemented, ultimately contributing to the advancement and optimization of AI-driven healthcare solutions

Social smart city research: interconnections between participatory governance, data privacy, artificial intelligence and ethical sustainable development

Social aspects constitute both concerns and opportunities in smart city development, as evidenced by a rapidly increasing body of research. This article presents the first-ever review of all the existing research on social focus in smart cities, delineating the distribution of topics, knowledge bases, and research frontiers that constitute the existing body of knowledge. A bibliometric review was performed to pinpoint publication trends, influential authors, their institutions, and prevalent subject areas within the literature since 2000. Using the Web of Science database, an amalgamation of major indexes (SCI-EXPANDED, SSCI, AHCI, ESCI) were applied to consider the research pattern and citation impact in different disciplines. 1,030 selected articles were subjected to bibliometric mapping using VOSviewer. The results show an almost exponential growth in the number of publications from 2015 onwards. Four interconnected thematic clusters cropped up: (1) participatory governance, (2) data privacy and security, (3) artificial intelligence and social media, and (4) ethics and sustainable development. A deeper analysis of key terms used in recent research revealed the following hot topics: (1) governance and citizen participation, (2) artificial intelligence technologies such as machine learning, (3) blockchain, and (4) Internet of Things. Co-authorship and geographical analyses underpin a solid international collaboration for leading institutions. The results underscore the rising significance of social smart city research by emphasizing the interconnectedness of governance, technology, citizen engagement, and ethics for a comprehensive approach to smart city initiatives. Furthermore, they recommend integrating social equity into these frameworks and enhancing geographic studies through greater international collaboration.

Evolution of banking systems: A comprehensive historical analysis

This paper provides a comprehensive historical analysis of the evolution of banking systems, tracing their origins from ancient Greece and Rome through medieval Europe, the Renaissance, and the emergence of modern banking institutions. It explores the foundational practices of ancient Greek "trapezitai" and Roman "argentarii," which laid the groundwork for more advanced systems that flourished in the Italian city-states during the Middle Ages and Renaissance. The development of commercial and central banking systems in early modern Europe, including the establishment of the Bank of Amsterdam and the Bank of England, is examined in detail. The study further highlights the role of colonial powers in shaping banking structures across the globe and discusses significant innovations of the 19th and 20th centuries, such as fractional reserves and the rise of investment banking. Emphasis is placed on the transformational impact of globalization, deregulation, and technological advancements in the modern banking era. Additionally, the paper delves into contemporary challenges, including cybersecurity, data privacy, and the integration of artificial intelligence, offering insights into the future trajectory of banking institutions. By bridging historical developments with modern trends, this analysis underscores the enduring significance of banking as a dynamic and essential component of economic systems, providing valuable lessons for understanding its future evolution.

Advancing Alzheimer's Treatment: Unleashing the Power of Donanemab and Quantum Computing for Enhanced Patient Outcomes

Donanemab is the first antibody to target pyroglutamate-modified amyloid-beta in Alzheimer's disease selectively; thus, it represents a significant breakthrough in disease-modifying treatments. Importantly, its mechanism of action encourages adequate clearance of plaques and does not even worsen outcomes for early-stage patients, in contrast to previous treatments that did not promote clearing for plaques or even worsened the outcomes of early-stage patients. The integration of quantum computing in drug discovery holds tremendous transformations in terms of enhancing the therapeutic approach against Alzheimer's disease. Researchers can speed up discovering novel compounds, optimize treatment regimens, and personalize patient care according to individual neurobiological profiles by using quantum computing powers. The letter to the editor discusses the unique attributes of Donanemab, its clinical superiority, and the related side effects, besides pushing for the promising future of integrating quantum computing into the paradigms of Alzheimer's treatment. Though promising, integrating quantum computing into medical practice is challenged by factors such as high computational costs, data privacy, and ethical considerations that must be taken within strict regulatory frameworks.

Navigating the Minefield: Ethical Challenges and Considerations in Data-Driven Management

In today’s contemporary business climate, data-driven management has emerged as an essential approach for organizations progressing towards streamlining overall operations, elevating decision-making, and promoting creativity. However, when the same data analytics assimilates with managerial applications, it leads to intricate ethical issues to deal with. This chapter examines vital ethical dimensions inherent in data-driven management such as data protection and privacy, liability, transparency, and possibilities of fraudulent use. The key concern is protecting the data collected while ensuring that it is collected with informed and mutual consent from the subjects. At the same time, maintaining transparency in data collection and its eventual utilization is also crucial to gaining and securing trust. Another significant challenge is the liability of companies and individuals to maintain and employ the data ethically. This ethical landscape becomes further complicated by data manipulation or misuse and the detrimental effects of predictive analysis. Businesses can levy data-driven management to promote efficiency and innovation as well as trust and integrity in their operations by proactively tackling these ethical challenges. The chapter unfolds a comprehensive review that delves into the ethical challenges and considerations of corporate decision-making based on data analytics and summarizes the ethical framework encompassing principles of transparency and accountability of businesses in the era of pervasive data-driven decision-making

Blockchain-enabled digital twin system for brain stroke prediction

A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions. Moreover, concerns around data security and privacy continue to challenge the widespread adoption of these models. To address these challenges, we developed a secure, machine learning powered digital twin application with three main objectives enhancing prediction accuracy, strengthening security, and ensuring scalability. The application achieved an accuracy of 98.28% for brain stroke prediction on the selected dataset. The data security was enhanced by integrating consortium blockchain technology with machine learning. The results show that the application is tamper-proof and is capable of detecting and automatically correcting backend data anomalies to maintain robust data protection. The application can be extended to monitor other pathologies such as heart attacks, cancers, osteoporosis, and epilepsy with minimal configuration changes.

<b>Machine Learning Enabled In-Home ECG: A Review</b>

Machine learning (ML)-based in-home electrocardiogram (ECG) systems have emerged as transformative tools, advancing beyond traditional cardiology methods by offering innovative techniques for cardiac care. These systems enable sustained data collection, real-time monitoring of cardiac status, and individualized treatment plans, all while minimizing the need for frequent clinic visits. By leveraging advanced analytics and ML algorithms, in-home ECG systems analyze large-scale datasets to detect patterns and anomalies that might otherwise go unnoticed, providing early alerts and improving patient outcomes. This review examines the latest trends in ML-enhanced in-home ECG technology, emphasizing its functionality in anomaly detection, continuous monitoring, and decision-making processes. The integration of ML not only enhances diagnostic precision but also opens avenues for scalable, personalized, and remote healthcare solutions. Despite these advancements, significant challenges remain, including issues related to data privacy, algorithmic biases, and the reliability of real-world implementations. Addressing these challenges is essential for optimizing the performance and ethical use of these systems. This review also explores opportunities for future research, particularly in improving algorithm robustness and addressing biases to ensure equitable and accurate cardiac care for diverse populations. By integrating state-of-the-art ML techniques, in-home ECG systems are poised to revolutionize contemporary cardiology, reducing healthcare costs and enabling a progressive shift toward accessible, patient-centered care. This comprehensive exploration highlights the potential of ML-based in-home ECG systems to redefine cardiac monitoring and treatment, contributing to the broader transformation of modern healthcare. Received: 13 September 2024 | Revised: 11 December 2024 | Accepted: 31 December 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Aqsa Bibi: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration. Jawwad Sami Ur Rahman: Methodology, Validation, Investigation, Resources, Writing - review & editing, Supervision, Project administration.

The Data Heterogeneity Issue Regarding COVID-19 Lung Imaging in Federated Learning: An Experimental Study

Federated learning (FL) has emerged as a transformative framework for collaborative learning, offering robust model training across institutions while ensuring data privacy. In the context of making a COVID-19 diagnosis using lung imaging, FL enables institutions to collaboratively train a global model without sharing sensitive patient data. A central manager aggregates local model updates to compute global updates, ensuring secure and effective integration. The global model’s generalization capability is evaluated using centralized testing data before dissemination to participating nodes, where local assessments facilitate personalized adaptations tailored to diverse datasets. Addressing data heterogeneity, a critical challenge in medical imaging, is essential for improving both global performance and local personalization in FL systems. This study emphasizes the importance of recognizing real-world data variability before proposing solutions to tackle non-independent and non-identically distributed (non-IID) data. We investigate the impact of data heterogeneity on FL performance in COVID-19 lung imaging across seven distinct heterogeneity settings. By comprehensively evaluating models using generalization and personalization metrics, we highlight challenges and opportunities for optimizing FL frameworks. The findings provide valuable insights that can guide future research toward achieving a balance between global generalization and local adaptation, ultimately enhancing diagnostic accuracy and patient outcomes in COVID-19 lung imaging.

Integrating Digital Literacy into Curriculum Design a Framework for 21st Century Learning

In the modern digital era, integrating digital literacy into curriculum design is critical to preparing students for 21st-century challenges. Digital literacy encompasses more than technical skills; it includes intellectual competence, ethical awareness, and adaptive behavior necessary for modern personal and professional environments. This study explores strategies for embedding digital literacy into educational frameworks, leveraging methodologies like digital storytelling and Inquiry-Based Learning (IBL). These approaches enable active, student-centered learning while fostering critical thinking, creativity, collaboration, and communication. Key findings highlight that technology, when properly implemented, enhances engagement and learning effectiveness by tailoring material to individual needs and encouraging experiential exploration. The research also underscores the necessity for institutional support, including teacher training, infrastructure, and equitable access to technology to minimize the digital divide. Despite its potential, challenges remain, such as resistance to educational change and concerns over student data privacy. This study contributes by proposing the TPACK framework, which integrates content knowledge, pedagogy, and technology, as a guide for comprehensive digital literacy integration. Future research should examine the long-term impacts of these practices on educational outcomes and refine tools and strategies for teacher development and technology application in classrooms.

AI and Machine Learning in The Cloud: This Involves Using AI and Machine Learning in Cloud Computing

Artificial intelligence (AI) and machine learning (ML) have emerged as disruptive technologies that are redefining cloud computing. The incorporation of AI and machine learning into cloud platforms improves productivity, scalability, and flexibility, allowing organizations to handle massive amounts of data and make intelligent choices in real time. This study investigates the symbiotic relationship between AI/ML and cloud computing, focusing on how cloud architecture provides the computational capacity needed for AI and ML models while AI improves cloud resource allocation. The article discusses key breakthroughs such as AI-driven automation in cloud operations, predictive analytics, and intelligent application deployment. The report emphasizes the democratization of AI capabilities via cloud services, which make them available to small and medium-sized organizations (SMEs) without requiring considerable infrastructure investment. It also investigates the security hurdles, ethical concerns, and compliance issues that come with data-intensive AI systems on the cloud. Real-world applications include AI-powered recommendation systems, fraud detection, and tailored consumer experiences, demonstrating the strength of this synergy. The report indicates that incorporating AI and ML into cloud computing is critical for expanding technological landscapes, driving creativity, and helping enterprises to remain competitive. However, addressing data privacy, ethical AI usage, and fair access to cloud-based AI resources are crucial for long-term progress. This study provides insights for researchers and practitioners on leveraging AI/ML in the cloud to meet evolving technological and business needs.

VFL-Cafe: Communication-Efficient Vertical Federated Learning via Dynamic Caching and Feature Selection

Vertical Federated Learning (VFL) is a promising category of Federated Learning that enables collaborative model training among distributed parties with data privacy protection. Due to its unique training architecture, a key challenge of VFL is high communication cost due to transmitting intermediate results between the Active Party and Passive Parties. Current communication-efficient VFL methods rely on using stale results without meticulous selection, which can impair model accuracy, particularly in noisy data environments. To address these limitations, this work proposes VFL-Cafe, a new VFL training method that leverages dynamic caching and feature selection to boost communication efficiency and model accuracy. In each communication round, the employed caching scheme allows multiple batches of intermediate results to be cached and strategically reused by different parties, reducing the communication overhead while maintaining model accuracy. Additionally, to eliminate the negative impact of noisy features that may undermine the effectiveness of using stale results to reduce communication rounds and incur significant model degradation, a feature selection strategy is integrated into each round of local updates. Theoretical analysis is then conducted to provide guidance on cache configuration, optimizing performance. Finally, extensive experimental results validate VFL-Cafe’s efficacy, demonstrating remarkable improvements in communication efficiency and model accuracy.

Two-Party Quantum Private Comparison Protocol Based on Rotational Encryption

In this paper, we introduce a two-party quantum private comparison (QPC) protocol that employs single photons as quantum resources and utilizes rotational encryption to safeguard the privacy of the inputs. This protocol enables two parties to compare their private data without disclosing any information beyond the outcome of the comparison. The participants’ private data are encoded as single photons, which are encrypted using a rotational encryption method. These encrypted single photons are then transmitted to a semi-honest third party (TP), who conducts single-particle measurements to determine if the users’ private data are equal and subsequently announces the results to the participants. By harnessing the principles of quantum mechanics, we ensure robust protection against potential eavesdropping and participant attacks. In contrast to numerous existing QPC protocols that rely on multi-qubit or d-dimensional quantum states, our method exhibits superior efficiency and practicality. Specifically, our protocol achieves a qubit efficiency of 50% by using two single photons to compare one bit of classical information, and single photons are easier to prepare than multi-qubit and d-dimensional quantum states.

Cybersecurity in Autonomous Systems: Protecting AI-Driven Applications

Autonomous systems, driven by advancements in AI, are transforming industries like transportation, healthcare, and manufacturing. These systems operate without constant human intervention, making them susceptible to various cyber threats. This paper addresses cybersecurity challenges specific to AI-driven autonomous systems and provides guidance on protecting these applications by focusing on secure communication, data privacy, and algorithm integrity. Effective approaches for implementing cybersecurity protocols in autonomous systems are also discussed to ensure data protection, system reliability, and compliance with evolving regulatory standards. Keywords Autonomous Systems, Cybersecurity, AI, Secure Communication, Algorithm Integrity, Data Privacy, Threat Modeling, Autonomous Vehicles, Drones, Robotics

Harnessing Artificial Intelligence for Environmental Sustainability: Ethical Considerations and Practical Implications in Achieving SDG 9 And SDG 16

Objectives: This study investigates the role of Artificial Intelligence (AI) in promoting environmental sustainability, exploring its applications in climate risk modeling, wildlife habitat monitoring, and renewable energy optimization. It also examines the ethical, moral, and legal implications of AI in achieving sustainable outcomes. Theoretical Framework: The research is grounded in the intersection of AI technology and sustainability, with a focus on justice, equity, and the principles of green AI. It aligns with the Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 16 (Peace, Justice, and Strong Institutions). Method: A comprehensive review of existing literature and case studies was conducted to analyze AI applications and their implications in environmental sustainability. Ethical considerations, including algorithmic bias, energy consumption, and data privacy, were critically examined. Results and Discussion: The findings highlight AI’s potential to optimize ecosystems, combat climate change, and improve resource utilization. However, challenges such as data discrimination, unequal access, and ethical concerns must be addressed. The discussion emphasizes the need for frameworks that integrate AI’s capabilities with sustainable practices to achieve equitable outcomes. Research Implications: This study underscores the importance of adopting strategic approaches to AI deployment, encouraging policymakers and businesses to align AI innovations with sustainability goals. Originality/Value: By addressing both the opportunities and challenges of AI in sustainability, this research provides a novel perspective on leveraging AI for environmental and societal benefits, offering actionable insights for future developments.