Data Privacy Research Articles

Utilizing Technology and Artificial Intelligence in Educational Administration to Enhance School Performance at Junior High School

This study aims to explore the use of technology and artificial intelligence (AI) in education administration to improve school performance. In the growing digital era, demands for efficiency and effectiveness in education administration are increasing, especially with the development of school information management systems (SIMS) and AI-based technologies. This research examines how these technologies can automate routine tasks such as class scheduling, student data management, as well as improve data-driven decision-making that is more accurate and efficient. The research method used in this study is descriptive qualitative, with the process of collecting data through in-depth interviews, observation, and document analysis directly from several schools that have adopted this technology. In analyzing the data, I used Miles and Hubberman's data triangulation technique in the form of data reduction, data analysis, and conclusion drawing. The results show that the implementation of technology and AI has improved operational efficiency, accelerated administrative processes, and facilitated the monitoring of student and staff performance. However, the study also found challenges in technology adoption, including limited infrastructure, resistance to change, and concerns regarding data privacy. To overcome these obstacles, a holistic strategy is needed, including the development of supportive policies, digital skills training for staff, and improvement of technology infrastructure in schools. This research concludes that although the application of technology and AI in education administration faces various barriers, the potential benefits are significant, especially in terms of improving the efficiency, transparency and overall quality of school performance.

Balancing Safety and Justice, the Ethics of Predictive Policing

Examining the ethical ramifications of predictive policing in South Africa, the research aims to pinpoint major obstacles and dangers while examining potential solutions to allay worries and guarantee responsible application. Key subjects include data privacy, algorithmic bias, over policing, community involvement, accountability and transparency, and transparency. reduction of bias, oversight procedures, Important Points, Significant ethical issues are brought up by predictive policing in South Africa, including invasions of privacy, algorithmic prejudice, and over policing. Given the historical and current backdrop of racial inequality, these worries are more pressing. In order to address these issues, a balanced strategy that puts individual rights and public safety first is needed. In order to guarantee that predictive policing is used responsibly in South Africa, transparency, accountability, community involvement, and ethical standards are crucial. It is essential to mitigate bias in algorithms and data in order to stop biased results. An over-reliance on predictive policing can be detrimental to community relations and conventional enforcement techniques. Methodological strategy: Designing qualitative research, review of the literature, semi-structured interviews with members of the public, law enforcement officers, and predictive policing specialists Case studies of South African predictive policing initiatives, Content study of South African media reports on predictive policing. Relevance. The work advances our knowledge of the moral conundrums raised by predictive policing in South Africa in a more comprehensive and nuanced way. The study can serve to raise public awareness and encourage participation in the discussion over predictive policing in South Africa. The findings can assist policymakers and law enforcement agencies in creating moral and practical strategies for implementing predictive policing. In conclusion, there are advantages and disadvantages to predictive policing in South Africa. Although technology presents the possibility of more effective and efficient law enforcement, it also brings up serious ethical issues. South Africa can minimize the dangers associated with predictive policing while maximizing its potential advantages by addressing these issues and putting the necessary procedures in place.

AI in Cytopathology: A Narrative Umbrella Review on Innovations, Challenges, and Future Directions

The integration of artificial intelligence (AI) in cytopathology is an emerging field with transformative potential, aiming to enhance diagnostic precision and operational efficiency. This umbrella review seeks to identify prevailing themes, opportunities, challenges, and recommendations related to AI in cytopathology. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements and future implications of AI technologies in this domain. Twenty-one review studies were selected through a systematic process. AI has demonstrated promise in automating and refining diagnostic processes, potentially reducing errors and improving patient outcomes. However, several critical challenges need to be addressed to realize the benefits of AI fully. This review underscores the necessity for rigorous validation, ongoing empirical data on diagnostic accuracy, standardized protocols, and effective integration with existing clinical workflows. Ethical issues, including data privacy and algorithmic bias, must be managed to ensure responsible AI applications. Additionally, high costs and substantial training requirements present barriers to widespread AI adoption. Future directions highlight the importance of applying successful integration strategies from histopathology and radiology to cytopathology. Continuous research is needed to improve model interpretability, validation, and standardization. Developing effective strategies for incorporating AI into clinical practice and establishing comprehensive ethical and regulatory frameworks will be crucial for overcoming these challenges. In conclusion, while AI holds significant promise for advancing cytopathology, its full potential can only be achieved by addressing challenges related to validation, cost, and ethics. This review provides an overview of current advancements, identifies ongoing challenges, and offers a roadmap for the successful integration of AI into diagnostic cytopathology, informed by insights from related fields.

Overcoming the Friction between the “Right to be Forgotten” and Blockchain Technology through a New Approach

Objective: this paper explores the challenges arising from the conflict between blockchain technology and the “right to be forgotten” as provided by the European data protection framework.Methods: in the First Section, the author provides a brief description of the evolution of blockchain technology and the most pressing issues between traditional blockchain models and UE’s legislations. Among the latter, the author analyzes the specific issue concerning the clash between the traditional blockchains (both private and public models), typically immutable, and the individual’s right to cancellation or modification of own personal data. This section emphasizes the importance of personal data protection, which has always been one of the main tasks for supranational legislators. The legal regulation of data protection and the relevant judicial practice of the European Court of Human Rights is analyzed. The author raises the problem of expressing the free self-determination of an individual in the form of controlling their personal data on the Internet. The Second Section of this contribution is dedicated to the study of probable ways to solve the existing incompatibility and to make the distributed ledger system compatible with the European data protection legislation. An emphasis is made on the model provided by “Traent” company, which ensures the right to data cancellation or modification. The capability of this model to solve the said contradiction is analyzed.Results: the study delves into the peculiar features of the new model to understand how it strategically utilizes the advantages of public and private blockchains guaranteeing not only the validity and authenticity of the chain where the transaction was performed, but, most importantly, the modification and granular cancellation of client’s personal data. This innovative solution offers a potential path forward for navigating the complex intersection of data privacy and blockchain innovation in the European context.Scientific novelty: Traent has implemented a “hybrid” model blockchain that, incorporating both public and private components, to achieve an effective compliance with the European Union regulations, especially those concerning data protection and privacy.Practical significance: the obtained conclusions and proposals can be taken into consideration in improving the compliance of blockchain technologies with the European Union General Data Protection Regulation.

Advancing language learning: The impact and challenges of Computer-Assisted Language Learning (CALL)

Abstract. Computer-Assisted Language Learning (CALL) represents a transformative approach in modern education, integrating technology to enhance language acquisition effectively. This comprehensive review examines the various dimensions of CALL, focusing on its deployment through interactive software, online courses, mobile applications, and the accompanying challenges. Quantitative analyses reveal significant improvements in learner engagement and cognitive load management due to multimedia and interactive content. Moreover, the adaptability of CALL to cater to individual learning needs through personalized and adaptive learning systems demonstrates a superior proficiency acquisition rate. Despite these advancements, CALL implementation faces substantial barriers including technological limitations, the need for extensive teacher training, and concerns regarding data privacy and security. This article assesses the statistical impact of these elements on language learning outcomes and suggests strategies for overcoming the associated challenges. Through mixed-methods research and longitudinal studies, the effectiveness of CALL platforms is substantiated, highlighting their pivotal role in reshaping language education across diverse demographic and geographic spectrums.

Integrating AI into Agile Workflows: Opportunities and Challenges

Abstract: Over the past few years, Agile development approaches have become an increasingly popular methodology for software engineering, focused on iterative progress, continuous feedback, and teamwork cooperation. However, the raising difficulties of projects and more demand for faster and more efficient workflows have challenged the situation further. The introduction of Artificial Intelligence into the Agile processes paves the way to optimize decision-making, automate routine tasks, and boost team productivity. This review summarizes the innovations and challenges created by the integration of AI into Agile development practices. Using AI technologies like machine learning, predictive analysis, and natural language processing, Agile teams can enhance sprint planning, resource coordination, and risk management. Also, it presents some risks such as data privacy, workforce skills need, and possible over-dependence of AI. The paper emphasizes on providing a full overview of the innovations and challenges to the application of AI in Agile workflows, providing thoughts for future research and practices.

Leveraging artificial intelligence in economics and finance: Enhancing decision-making and market efficiency

Abstract. Artificial Intelligence (AI) has revolutionized the fields of economics and finance by providing advanced tools for decision-making, predictive analysis, and market efficiency. This paper examines the integration of AI technologies such as machine learning and natural language processing with mathematical models, specifically ARIMA for economic forecasting, Black-Scholes for option pricing, and logistic regression for credit risk assessment. By enhancing these models with AI, we demonstrate significant improvements in prediction accuracy and decision-making capabilities. Case studies illustrate a 15% improvement in GDP growth prediction accuracy, a 20% reduction in option pricing errors, and a 20% decrease in credit default rates. The paper also discusses the future prospects of AI, including advancements in quantum computing and ethical considerations like data privacy and algorithmic bias. Implementation challenges such as high costs and data integration issues are addressed, providing a comprehensive roadmap for organizations to effectively leverage AI. This study underscores the transformative potential of AI in shaping the future of economic and financial landscapes, driving innovation and operational efficiency.

Harnessing Artificial Intelligence for Personalized Learning Pathways: A Framework for Adaptive Education Management Systems

Abstract. The integration of artificial intelligence (AI) into education is revolutionising how we deliver personalised learning, where curriculum, pedagogy and assessments are dynamically adjusted to meet the demands of each learner. In this paper, we examine the role AI can play in the construction of personalised learning pathways, and explore its applications for K-12 education, higher education and lifelong learning. We highlight that AI-driven systems such as Summit Learning, Pounce at Georgia State University, and IBMs Watson Talent are helping to measure dramatic improvements in student and employee outcomes, ranging from retention rates, academic performance, to engagement levels. We show how AI can enable student profiling, adaptive learning path generation, and ongoing assessment via the continuous feedback loops that are critical to transformative personalised learning. We also highlight some of the key challenges related to how education (eg, data privacy, scalability and equity), and showcase case studies and data from AI applications in different educational contexts to demonstrate the potential of personalised learning enabled by AI. In summary, this paper underscores the benefits and challenges of implementing AI in education, providing a perspective on how AI is likely to shape the future of learning.

Revolutionizing education with AI: The adaptive cognitive enhancement model (ACEM) for personalized cognitive development

Abstract. The integration of artificial intelligence (AI) into education has opened doors to personalized learning experiences. This paper introduces the Adaptive Cognitive Enhancement Model (ACEM), a cutting-edge AI-driven framework designed to personalize cognitive development for students. Leveraging advanced machine learning algorithms and quantitative analysis, ACEM adapts educational content and learning strategies to individual cognitive needs. The model encompasses five key components: cognitive profiling, adaptive learning paths, intelligent feedback, motivational strategies, and longitudinal tracking. Through quantitative analysis and mathematical modeling, the paper demonstrates how ACEM significantly enhances learning outcomes compared to traditional education models. The discussion section provides a detailed exploration of each model component, its architecture, and its role in optimizing personalized cognitive development. Furthermore, challenges such as data privacy, scalability, and model interpretability are examined, alongside potential solutions. The conclusion underscores the transformative potential of ACEM in revolutionizing education.

Ethical considerations in telehealth and artificial intelligence for work related musculoskeletal disorders: A scoping review.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to perform tasks that typically require human intelligence. The integration of AI and telehealth applications in healthcare raises ethical concerns such as bias, transparency, data privacy, and accountability for errors. Several studies have assessed this topic, particularly with regard to musculoskeletal disorders, which will be the focus of this manuscript. We will examine key ethical concepts including informed consent, data protection, confidentiality, physician malpractice, liability, and telemedicine regulations. Ethical issues pertaining to the topic were explored through a review paper. The primary objective of this scoping review was to map and synthesize the existing literature concerning ethical considerations in telehealth and AI for work-related musculoskeletal disorders. Research demonstrates that medication effectiveness, patient and physician satisfaction, and accessibility costs are higher with telemedicine and AI methods compared to in-person approaches, particularly for work-related musculoskeletal disorders. Therefore, addressing ethical issues, including patient data privacy and security, is crucial in this field. By considering these factors, the adoption of emerging AI and telemedicine applications, especially for work-related musculoskeletal disorders, is likely to increase. AI and telemedicine offer significant advantages, particularly in addressing work-related musculoskeletal disorders. However, ethical and legal issues surrounding their practice require standardized rules to ensure equitable access, quality care, sustainable costs, professional liability, patient privacy, data protection, and confidentiality. Further practical research studies are needed to address these considerations more effectively.

Cloud-Based Machine Learning : Opportunities and Challenges

This comprehensive article explores the transformative impact of cloud-based machine learning (ML) on modern enterprises, examining both opportunities and challenges in implementation. The article investigates the rapidly growing cloud computing market and its ML segment, revolutionizing how organizations approach data analytics and business intelligence. Through detailed analysis of enterprise implementations, the article demonstrates how cloud ML solutions have democratized access to advanced analytics, significantly reducing operational costs while improving data processing efficiency. The article examines key aspects, including scalability advantages, cost efficiencies, and technical complexities, while providing evidence-based best practices for successful implementation. Drawing from multiple industry studies and real-world deployments, the article presents a framework for organizations to navigate challenges in data privacy, vendor dependencies, and skill requirements while maximizing the benefits of cloud-based ML solutions.

Occupational therapy in the space of artificial intelligence: Ethical considerations and human-centered efforts

Background Artificial intelligence (AI) technology is constantly and rapidly evolving and has the potential to benefit occupational therapy (OT) and OT clients. However, AI developments also pose risks and challenges, for example in relation to the ethical principles of OT. One way to support future AI technology aligned with OT ethical principles may be through human-centered AI (HCAI), an emerging branch within AI research and developments with a notable overlap of OT values and beliefs. Objective To explore the risks and challenges of AI technology, and how the combined expertise, skills, and knowledge of OT and HCAI can contribute to harnessing its potential and shaping its future, from the perspective of OT’s ethical values and beliefs. Results Opportunities for OT and HCAI collaboration related to future AI technology include ensuring a focus on 1) occupational performance and participation, while taking client-centeredness into account; 2) occupational justice and respect for diversity, and 3) transparency and respect for the privacy of occupational performance and participation data. Conclusion and Significance There is need for OTs to engage and ensure that AI is applied in a way that serves OT and OT clients in a meaningful and ethical way through the use of HCAI.

Systematic review of ethics and legislation of a Global Patient co-Owned Cloud (GPOC)

Background The use of cloud-based storage for personal health records (PHRs) has significantly increased globally over the past thirty years. The four recently published GPOC Series articles introduced the concept of a Global Patient co-Owned Cloud (GPOC) for personal health records. The series includes a systematic review and meta-analysis, a summit, a sandbox, and a survey, with 100% participation from UN member states and key international health organisations. GPOC aims to establish patient co-ownership of PHRs, addressing integration and access challenges. Methods This study is built upon the published GPOC systematic review and meta-analysis that focused on examining cloud-based personal health records and elements such as data security, efficiency, performance, privacy and cost-based factors. However, this study selectively reviews the ethical, legislative and potential human rights dimensions of GPOC. Thus, it includes ethical aspects of co-ownership, rights, privacy, policies, and AI integration. The original study was PROSPERO registered with CRD42022342597, which serves as the foundation for the current study. Results This study offers a comprehensive global overview of ethics, legislation and initiatives by states and organisations. We analyse AI integration and future challenges for GPOC implementation. We present principles from ‘Ethics by Design’ and the ‘Principles of Biomedical Ethics’ by Beauchamp and Childress alongside the European General Data Protection Regulation (GDPR). The study presents a global overview of the relevant global latticework of legislation. Conclusions This study suggests that GPOC could potentially establish a new human right to patient co-ownership of personal health information. GPOC aims to facilitate global AI integration in healthcare and address existing challenges in PHR integration. A decentralised GPOC, supported by blockchain consensus, may offer benefits such as enhanced data security, interoperability, and equitable access to healthcare information globally. Thus, GPOC may have a positive impact on global health.

Threshold Filtering for Detecting Label Inference Attacks in Vertical Federated Learning

Federated learning, as an emerging machine-learning method, has received widespread attention because it allows users to train locally during the training process and uses relevant cryptographic knowledge to safeguard the privacy of data during model aggregation. However, existing federated learning is also susceptible to privacy breaches, e.g., label inference attacks against vertical federated learning scenarios, where an adversary is able to reason about the labels of other participants based on the trained model, leading to serious privacy breaches. In this paper, we design a detection method for label inference attacks in vertical federated learning scenarios, which is able to detect the attacks based on the principles of the attacks. We design a threshold-filtering detection method based on the principle of attack to determine that the model is under attack when the threshold value is greater than a set parameter. Furthermore, we have created six threat model classifications based on different a priori conditions of the adversary to comprehensively analyze the adversary’s attacks. In addition to the detection method of attacks, the extent of attacks on the model and the effectiveness of the defense can also be evaluated. The evaluation module will experimentally measure the changes in the relevant metrics such as the accuracy of the attack, the F1 score, and the change in the accuracy after the defense method. For example, detection in the full connected neural network model assesses the attack and defense effectiveness of the model with an attack accuracy of 86.72% in the breast cancer Wisconsin dataset and an F1 score of 0.743, which is reduced to 36.36% after dispersed training. This ensures that users have an overall grasp of the extent to which the training model is under attack before deploying the model.

Personalized cancer vaccine design using AI-powered technologies

Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. While current cancer vaccines are primarily prophylactic, advancements in targeting tumor-associated antigens (TAAs) and neoantigens have paved the way for therapeutic vaccines. The integration of artificial intelligence (AI) into cancer vaccine development is revolutionizing the field by enhancing various aspect of design and delivery. This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses. By utilizing AI technologies, researchers can navigate complex biological datasets and uncover novel therapeutic targets, thereby improving the precision and efficacy of cancer vaccines. Despite the promise of AI-powered cancer vaccines, significant challenges remain, such as tumor heterogeneity and genetic variability, which can limit the effectiveness of neoantigen prediction. Moreover, ethical and regulatory concerns surrounding data privacy and algorithmic bias must be addressed to ensure responsible AI deployment. The future of cancer vaccine development lies in the seamless integration of AI to create personalized immunotherapies that offer targeted and effective cancer treatments. This review underscores the importance of interdisciplinary collaboration and innovation in overcoming these challenges and advancing cancer vaccine development.

Interpreting Federated Learning (FL) Models on Edge Devices by Enhancing Model Explainability with Computational Geometry and Advanced Database Architectures

Federated learning (FL) on edge devices has emerged as a promising approach for decentralized model training, enabling data privacy and efficiency in distributed networks. However, the complexity of these models presents significant challenges in terms of transparency and interpretability, which are critical for trust and accountability in real-world applications. This paper explores the integration of explainable AI techniques to enhance model interpretability within federated learning systems. By incorporating computational geometry, we aim to optimize model structure and decision-making processes, providing clearer insights into how models generate predictions. Additionally, we examine the role of advanced database architectures in managing the complexity of federated learning models on edge devices, ensuring efficient data handling and storage. Together, these approaches contribute to a more transparent, efficient, and scalable framework for federated learning on edge networks, addressing key challenges in both model explainability and performance optimization. This review highlights recent advancements and suggests future directions for research at the intersection of federated learning (FL), edge computing, explainability, and computational techniques.

Cryptography and DRM: A study of digital copyright protection in the gaming industry

Abstract. This paper explores the role of cryptography and encryption technology in the evolution of digital rights management (DRM) in the gaming industry. A collection of guidelines, procedures, and instruments that control the appropriate use of digital content is collectively referred to as digital rights management (DRM). Cryptography plays a vital role in DRM by ensuring data privacy, authenticity, and integrity. The early stages of DRM in gaming involved physical disc-based methods, such as unique disc features and online activation. However, these methods faced challenges such as compatibility issues and unauthorized copying. Modern DRM techniques incorporate advanced cryptographic techniques and account-based DRM tied to user accounts. Cryptography in DRM secures the distribution and consumption of digital content by converting game data into an unreadable format, which can only be decrypted with a valid key. The paper also discusses the use of third-party DRM technologies like Denuvo, which employ robust encryption and obfuscation methods. However, third-party DRM can have performance impacts, inconvenience users, and lead to compatibility issues. The paper emphasizes the need for continuous refinement of DRM technologies to balance copyright protection and consumer rights in the evolving gaming industry.

Artificial Intelligence and Corporate Social Responsibility: Synergies, Challenges, and Future Directions

Corporate social responsibility, or CSR, has been the subject of an increasing amount of interest with the infusion of artificial intelligence or AI. AI may open up much-needed improvements in CSR practice, including better decision-making, transparency, and sustainable benefits, but it also poses challenges related to the ethical concerns of data privacy, bias, and accountability. This paper focuses on how an AI-based approach enhances CSR activities and identifies risks embedded in AI adoption in approaches to CSR. The study combines qualitative interviews with industry experts and quantitative analysis of CSR reports from AI-adopting firms and thus sheds lights on the upside and downside of AI-enhanced CSR. Finally, this article ends with recommendations in developing a framework that would align AI capabilities with CSR objectives and indicates how that may potentially challenge future research directions in order to mitigate the ethical challenges involved.

AI Use in Application Modernization for Banking Applications

Implementing Artificial Intelligence (AI) and machine Learning (ML) into application modernization will shake up various industries, including finance, healthcare, manufacturing, e-commerce, and retail. This white paper will analyze the transformative potential of AI and ML in modernizing legacy systems and applications, enhancing innovation, operational efficiency, and security during modernization. By leveraging AI technologies like machine learning, natural language processing, and deep learning, organizations can streamline processes, enhance decision- making in the planning phase, and reduce errors during application modernization. The paper also addresses the challenges and considerations in planning and implementing application modernization in banks, as AI and ML have been increasingly adopted in the banking sector. AI and ML help with planning in Hollow the core, including data privacy, ethical concerns, and the need for a skilled workforce.

Empowering big data analytics through machine learning: Applications, challenges, and future directions

Abstract. This paper explores the transformative role of machine learning (ML) methodologies in big data analytics, highlighting supervised learning, unsupervised learning, and deep neural networks' contributions to diverse sectors. Supervised learning, with regression analysis at its core, provides accurate forecasting in finance and healthcare by modeling relationships between variables. Unsupervised learning, through techniques like k-means and hierarchical clustering, uncovers patterns within data, offering insights for retail and biological analysis. Deep learning, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), excel in complex tasks like image recognition and sequential data processing, driving advances in fields from autonomous driving to language translation. The application of ML in enhancing business intelligence, innovating fintech, and advancing healthcare analytics is discussed, alongside the challenges of data privacy, security, ethical considerations, and the skills gap. This paper underscores the need for advanced cryptographic techniques, bias mitigation strategies, and education to address these challenges. It concludes by emphasizing the importance of interdisciplinary education and AI advancements in bridging the skills gap, ensuring the ethical use of ML, and making these technologies accessible for future innovations.