Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data by Ashkan Pirmani, Edward De Brouwer, Ádám Arany, Martijn Oldenhof, Antoine Passemiers, Axel Faes, Tomas Kalincik, Serkan Ozakbas, Riadh Gouider, Barbara Willekens, Dana Horakova, Eva Kubala Havrdova, Francesco Patti, Alexandre Prat, Alessandra Lugaresi, Valentina Tomassini, Pierre Grammond, Elisabetta Cartechini, Izanne Roos, Cavit Boz, Raed Alroughani, Maria Pia Amato, Katherine Buzzard, Jeannette Lechner-Scott, Joana Guimarães, Claudio Solaro, Oliver Gerlach, Aysun Soysal, Jens Kuhle, Jose Luis Sanchez-Menoyo, Daniele Spitaleri, Tunde Csepany, Bart Van Wijmeersch, Radek Ampapa, Julie Prevost, Samia J. Khoury, Vincent Van Pesch, Nevin John, Davide Maimone, Bianca Weinstock-Guttman, Guy Laureys, Pamela McCombe, Yolanda Blanco, Ayse Altintas, Abdullah Al-Asmi, Justin Garber, Anneke Van der Walt, Helmut Butzkueven, Koen de Gans, Csilla Rozsa, Bruce Taylor, Talal Al-Harbi, Attila Sas, Cecilia Rajda, Orla Gray, Danny Decoo, William M. Carroll, Allan G. Kermode, Marzena Fabis-Pedrini, Deborah Mason, Angel Perez-Sempere, Mihaela Simu, Neil Shuey, Bhim Singhal, Marija Cauchi, Todd A. Hardy, Sudarshini Ramanathan, Patrice Lalive, Carmen-Adella Sirbu, Stella Hughes, Tamara Castillo Trivino, Liesbet M. Peeters, Yves Moreau

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Impact of perceived privacy and security in the TAM model: The perceived trust as the mediated factors by Yan Zhang

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Using Large Language Models to Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study by Denise Lee, Akhil Vaid, Kartikeya M Menon, Robert Freeman, David S Matteson, Michael L Marin, Girish N Nadkarni

“…However, the use of LLMs in the health care setting is limited by cost, computing power, and patient privacy concerns. …”
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Personal Information Sharing Behavior Using Social Media by Ashraf Sharif, Shafiq Ur Rehman, Khalid Mahmood

“…This study explores personal information sharing behavior publication patterns and trends on social media from 2007-2024 with an aim to highlight the annual growth of personal information sharing behavior (PISB) on social media platforms, key patterns in the PISB literature in terms of frequently cited authors, countries, institutions, sources, highly cited papers, collaboration and authorship patterns, thematic evolution, keyword and key factor analysis (such as countries, sources, and keywords). We used Scopus database for data extraction, and 1020 pertinent records were chosen. …”
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Ethical and secure evidence generation from regionwide clinical data through a collaborative environment for advancing predictive care by Dolores Muñoyerro-Muñiz, Román Villegas, Víctor de la Oliva, Alberto Esteban-Medina, Patricia Fernández del Valle, Ana Sánchez, M. Belen Susin, Isidoro Gutierrez-Alvarez, Marta Reboredo, Laura Alejos, Carlos Loucera, Carlos Loucera, Joaquín Dopazo, Joaquín Dopazo

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Application of novel security technique in cloud environment using attribute dependent authentication for health care by D. Deena Rose, C. Agees Kumar

“…A new Matrix based encryption algorithm (M-EA), is proposed in this research, for quick computational processing, effective and reliable data storage in the cloud. This research proposed a enhanced model for attainment of data privacy on the cloud, through the use of Attribute Dependent Multi Factor Authentication (ADMFA). …”
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Mobile Phone Network Data in the COVID-19 era: A systematic review of applications, socioeconomic factors affecting compliance to non-pharmaceutical interventions, privacy implicat... by Mohammed Okmi, Tan Fong Ang, Muhammad Faiz Mohd Zaki, Chin Soon Ku, Koo Yuen Phan, Irfan Wahyudi, Lip Yee Por

“…<h4>Background</h4>The use of traditional mobility datasets, such as travel surveys and census data, has significantly impacted various disciplines, including transportation, urban sensing, criminology, and healthcare. …”
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Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring by Kalashtari Niloofar, Huhs Niklas, Kraitl Jens, Hornberger Christoph, Simanski Olaf

“…By training a YOLOv5 deep learning model using transfer learning, a method for accurate human detection and tracking within rooms using data collected from a digital LiDAR sensor was used. …”
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Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis by Raja Waseem Anwar, Mohammad Abrar, Abdu Salam, Faizan Ullah

“…Using an FL approach, multiple IoT nodes collaboratively train a global LSTM model without exchanging raw data, thereby addressing privacy concerns and improving detection capabilities. …”
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Federated learning in food research by Zuzanna Fendor, Bas H.M. van der Velden, Xinxin Wang, Andrea Jr. Carnoli, Osman Mutlu, Ali Hürriyetoğlu

“…The use of machine learning in food research is sometimes limited due to data sharing obstacles such as data ownership and privacy requirements. …”
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Federated meta learning: a review by Chuanyao ZHANG, Shijing SI, Jianzong WANG, Jing XIAO

“…With the popularity of mobile devices, massive amounts of data are constantly produced.The data privacy policies are becoming more and more specified, the flow and use of data are strictly regulated.Federated learning can break data barriers and use client data for modeling.Because users have different habits, there are significant differences between different client data.How to solve the statistical challenge caused by the data imbalance becomes an important topic in federated learning research.Using the fast learning ability of meta learning, it becomes an important way to train different personalized models for different clients to solve the problem of data imbalance in federated learning.The definition and classification of federated learning, as well as the main problems of federated learning were introduced systematically based on the background of federated learning.The main problems included privacy protection, data heterogeneity and limited communication.The research work of federated metalearning in solving the heterogeneous data, the limited communication environment, and improving the robustness against malicious attacks were introduced systematically starting from the background of federated meta learning.Finally, the summary and prospect of federated meta learning were proposed.…”
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The Use of Artificial Intelligence in Combating Financial Crimes and Money Laundering in International Trade A Data-Driven Analysis (2010–2024) by Balouz Mohamed

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ETHICAL DIMENSIONS OF HUMAN RESOURCES AUDITING IN THE DIGITAL ERA by Alice STEFANESCU, Daria MARIN

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Determinants of Childbirth Choice in Rural Senegal: Mixed-Methods Analysis Using Data from the Niakhar Demographic Surveillance System by Pape Latyr Faye

“…Materials and methods: The study used a mixed-methods approach. Data from women who gave birth in the Niakhar observatory area between 1983 and 2020 were used, and chi-square tests and qualitative analyses were performed. …”
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Artificial intelligence in neuroimaging: Opportunities and ethical challenges by Neha Brahma, S. Vimal

“…Issues such as algorithmic bias, data privacy, and the interpretability of AI-driven insights must be addressed to ensure that these technologies are used responsibly and equitably. …”
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Improved SpaceTwist privacy protection method based on anchor optimization algorithm by Zhen-peng LIU, Xuan ZHAO, Ya-wei DONG, Bin ZHANG

“…With location-based services worldwide used,private location data appealed easily in query process which caused serious security problems.So the introduction of SpaceTwist incremental nearest neighbor query algorithm,proposes protection of privacy method combined with improved SpaceTwist location optimization algorithm.The anchor point authentication server added to distributed system structure,user generate a k anonymous area according to their privacy preference and actual environment,using optimization algorithm to generate the anchor point.Forwarding users use the incremental nearest neighbor query throught the anchor point and accurate.Experiments in road network environment with different data sets show that the privacy protection works well in the algorithm,and own high work efficiency.…”
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Verticox+: vertically distributed Cox proportional hazards model with improved privacy guarantees by Florian van Daalen, Djura Smits, Lianne Ippel, Andre Dekker, Inigo Bermejo

“…Abstract Federated learning allows us to run machine learning algorithms on decentralized data when data sharing is not permitted due to privacy concerns. …”
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Blockchain-Enabled Zero Trust Architecture for Privacy-Preserving Cybersecurity in IoT Environments by Mohammed A. Aleisa

“…It then uses blockchain technology for recording unalterable data of identity and access management while Zero-Knowledge Proofs (ZKP) ensures authentication and verification without revealing sensitive information. …”
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FedDBO: A Novel Federated Learning Approach for Communication Cost and Data Heterogeneity Using Dung Beetle Optimizer by Dongyan Wang, Limin Chen, Xiaotong Lu, Yidi Wang, Yue Shen, Jingjing Xu

“…After aggregating the model parameters sent by clients, the server performs a second iterative training on the aggregated model using its own metadata, thereby reducing data heterogeneity and improving model performance. …”
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Comparative Analysis of RAG-Based Open-Source LLMs for Indonesian Banking Customer Service Optimization Using Simulated Data by Hendra Lijaya, Patricia Ho, Handri Santoso

“…These findings underscore the potential of locally operated open-source LLMs for banking applications, ensuring privacy and regulatory compliance. However, limitations include reliance on synthetic data, a narrow question set, and lack of user diversity. …”
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