Enhancing Approach for Information Security in Hadoop by Raghad Z. Yousif, Shahab W. Kareem, Shadan M. Abdalwahid

“…This event motivates the need of a policy which will control these challenges. Hadoop may be a used policy recommended to beat this big data problem which usually utilizes MapReduce design to arrange huge amounts of information of the cloud system. …”
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The Association of Psychological Factors With Willingness to Share Health-Related Data From Technological Devices: Cross-Sectional Questionnaire Study by Marijn Eversdijk, Emma Rixt Douma, Mirela Habibovic, Willem Johan Kop

“…However, the use of health technology data might be hampered by the reluctance of patients to share personal health technology data because of the privacy sensitivity of this information. …”
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Santé et Big Data : l’Etat et les individus, impuissants face aux pouvoirs des réseaux by Joël Colloc

“…The use of Internet and of the social networks, lead the users to share data concerning their private life and their health. …”
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FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting by He Zhu, Jun Bai, Na Li, Xiaoxiao Li, Dianbo Liu, David L. Buckeridge, Yue Li

“…Abstract Federated learning (FL) enables collaborative analysis of decentralized medical data while preserving patient privacy. However, the covariate shift from demographic and clinical differences can reduce model generalizability. …”
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Determinants of Attitude and Intention to Use Virtual Credit Cards in Indonesia by Budi Hartono, Suci Nasehati Sunaningsih, Mumpuni Wahyudiarti Sitoresmi

“…Data analysis was conducted using path analysis, revealing that all variables significantly influence trust and perceived risk, with t-statistics below 0.05 and 0.01. …”
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Navigating the future of higher education in Saudi Arabia: implementing AI, machine learning, and big data for sustainable university development by Muhammad Adnan Khan, Abdur Rehman, Asghar Ali Shah, Sagheer Abbas, Meshal Alharbi, Munir Ahmad, Taher M. Ghazal

“…This paper looks at ways to successfully use artificial intelligence (AI) and big data analytics in Saudi higher education while supporting long-term growth. …”
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Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications by Sita Rani, Raman Kumar, B. S. Panda, Rajender Kumar, Nafaa Farhan Muften, Mayada Ahmed Abass, Jasmina Lozanović

“…It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. …”
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Bridging Data Silos in Oncology with Modular Software for Federated Analysis on Fast Healthcare Interoperability Resources: Multisite Implementation Study by Jasmin Ziegler, Marcel Pascal Erpenbeck, Timo Fuchs, Anna Saibold, Paul-Christian Volkmer, Guenter Schmidt, Johanna Eicher, Peter Pallaoro, Renata De Souza Falguera, Fabio Aubele, Marlien Hagedorn, Ekaterina Vansovich, Johannes Raffler, Stephan Ringshandl, Alexander Kerscher, Julia Karolin Maurer, Brigitte Kühnel, Gerhard Schenkirsch, Marvin Kampf, Lorenz A Kapsner, Hadieh Ghanbarian, Helmut Spengler, Iñaki Soto-Rey, Fady Albashiti, Dirk Hellwig, Maximilian Ertl, Georg Fette, Detlef Kraska, Martin Boeker, Hans-Ulrich Prokosch, Christian Gulden

“…The pipeline handles diverse IT infrastructures and systems while maintaining privacy by keeping data decentralized for analysis. …”
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Separable Reversible Data Hiding in Encrypted 3D Mesh Models Based on Spatial Clustering and Multi-MSB Prediction by Y. Mao, Y. Xu

“…Reversible data hiding in the encrypted domain (RDH-ED) involves performing data encryption to protect privacy and hiding data for covert communication or access control. …”
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Securing IoMT data with Algorand blockchain, XChaCha20-Poly1305 encryption, and decentralized storage alternatives by Divya K, Uma Priyadarsini P.S

“…Abstract Growing applications of Internet of Medical Things (IoMT) devices have revolutionized the healthcare sector because of remote patient tracking, diagnosis, and data-supported decision-making. The kind of medical data collected from these devices, however, is very sensitive, which makes it very vulnerable to issues of security, privacy, and integrity. …”
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The Use of Artificial Intelligence in Medical Diagnostics: Opportunities, Prospects and Risks by Nataliia Sheliemina

“…The AI integration in healthcare can revolutionise the industry by improving patient outcomes, optimising resource allocation, and reducing operational costs. However, the AI use in medicine carries certain risks related to ethics and data privacy, shortcomings in the quality of data for training algorithms, and importance of protecting against cyberthreats. …”
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Ethical aspects of the use of social networks during the employment process by Branislav Sančanin

“…Employers violate the privacy of potential candidates by obtaining data in this manner without making prior notification and receiving consent, which opens up a whole range of complex ethical and legal issues, especially in the light of the large number of fake profiles and pages on social networks. …”
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The ethics of using artificial intelligence in medical research by Shinae Yu, Sang-Shin Lee, Hyunyong Hwang

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Multifaceted Assessment of Responsible Use and Bias in Language Models for Education by Ishrat Ahmed, Wenxing Liu, Rod D. Roscoe, Elizabeth Reilley, Danielle S. McNamara

“…However, due to the nature of the training data, these models are susceptible to inherent social or cognitive biases, which can influence their outputs. …”
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An Evaluation on the Potential of Large Language Models for Use in Trauma Triage by Kelvin Le, Jiahang Chen, Deon Mai, Khang Duy Ricky Le

“…Overall, the literature highlights multifaceted applications of LLMs, especially in emergency trauma settings, albeit with clear limitations and ethical considerations, such as artificial hallucinations, biased outputs and data privacy issues. There remains room for more rigorous research into refining the consistency and capabilities of LLMs, ensuring their effective integration in real-world trauma triaging to improve patient outcomes and resource utilisation.…”
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Legal Challenges of Marine Insurance Laws in the Use of Unmanned Ships by Mahsa Bahadoran- Baghbaderani, Masoud Shirani, Reza Soltani

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Considerations for using Artificial Intelligence in Medical Education by Ali Hamidi Madani

“…Dear Editor Today, the traditional context and method of education have changed, marking the beginning of the digital era in education. The use of new technologies, such as virtual reality (VR), augmented reality (AR), simulators, and artificial intelligence (AI), is a key factor in improving the quality of education and learning outcomes. …”
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Is Anonymization Through Discretization Reliable? Modeling Latent Probability Distributions for Ordinal Data as a Solution to the Small Sample Size Problem by Stefan Michael Stroka, Christian Heumann

“…The growing interest in data privacy and anonymization presents challenges, as traditional methods such as ordinal discretization often result in information loss by coarsening metric data. …”
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Tanggung Jawab Perusahaan Terhadap Kebocoran Data Pribadi Dari Penggunaan Teknologi Robotic Process Automation (RPA) Menurut Hukum Positif di Indonesia by Yuni Zakira, Busyra Azheri, Anton Rosari

“… The constitutional basis regarding the importance of personal data protection is found in Article 28G paragraph (1) of the 1945 Constitution, providing a fundamental basis to guarantee the right to privacy, particularly the protection of personal data. …”
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Model Complexity Reduction for ZKML Healthcare applications by Sathya Krishnasamy, Ilangovan Govindarajan

“…As an extension, this paper discusses how one such enabler, ZKML, combines two streams of technology that are merging in unique ways to address problems in privacy and the cost of inference. The authors have conceptualized the technical and operational feasibility and implemented a reference healthcare implementation using the synthetic ICHOM in the evaluation phase in a global healthcare setting for high-volume data collection, including patient-reported outcomes. …”
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