Novel Federated Graph Contrastive Learning for IoMT Security: Protecting Data Poisoning and Inference Attacks by Amarudin Daulay, Kalamullah Ramli, Ruki Harwahyu, Taufik Hidayat, Bernardi Pranggono

“…By preserving patient data privacy at the edge, FedGCL enhances system resilience without sacrificing model performance. …”
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Quantitative and Spatially Explicit Clustering of Urban Grocery Shoppers in Montreal: Integrating Loyalty Data with Synthetic Population by Duo Zhang, Laurette Dubé, Antonia Gieschen, Catherine Paquet, Raja Sengupta

“…While integrating loyalty data with synthetic populations mitigates privacy concerns, challenges remain regarding data sparsity and demographic inconsistencies. …”
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Inferring Travel Modes from Cellular Signaling Data Based on the Gated Recurrent Unit Neural Network by Yanchen Wang, Fei Yang, Li He, Haode Liu, Li Tan, Cheng Wang

“…Moreover, in cooperation with the operator, we organized an experiment collecting cellular signaling data, as well as the corresponding GPS data. Using the collected dataset as ground-truth data, the performance of the method presented in this paper and other popular methods is verified and compared. …”
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Integrating human biomonitoring exposure data into a primary care morbidity database: a feasibility study by Pieter Jansen, Elly Den Hond, Katleen De Brouwere, Endale Alemayehu Ali, Hamid Yimam Hassen, Ilona Gabaret, Gijs Van Pottelbergh

“…This feasibility study uses exposure data from one of the completed PFAS HBM studies, which included 796 individuals. …”
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Integrating patient metadata and pathogen genomic data: advancing pandemic preparedness with a multi-parametric simulator by Bonjean Maxime, Ambroise Jérôme, Orchard Francisco, Sentis Alexis, Hurel Julie, Hayes Jessica S., Connolly Máire A., Jean-Luc Gala

“…However, data sharing between EU member states faces challenges due to disparities in data collection practices, standardisation, legal frameworks, privacy, security regulations, and resource allocation. …”
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A review on the impact of big data analytics in transforming agricultural practices, food processing, and preservation strategies by Jelili Babatunde Hussein, Tilahun Seyoum Workneh, Alaika Kassim, Khuthadzo Ntsowe, Sileshi F. Melesse, Hany S. El-Mesery

“…Big data is used a lot in food distribution and retail, but not enough in production, making decisions, and making things last. …”
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Obfuscated Malware Detection and Classification in Network Traffic Leveraging Hybrid Large Language Models and Synthetic Data by Mehwish Naseer, Farhan Ullah, Samia Ijaz, Hamad Naeem, Amjad Alsirhani, Ghadah Naif Alwakid, Abdullah Alomari

“…The first step involves generating synthetic TCP malware traffic data with malicious content using GPT-2. These data are then preprocessed and used in the second step, which focuses on malware classification. …”
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Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study by Mohammed A Almanna, Lior M Elkaim, Mohammed A Alvi, Jordan J Levett, Ben Li, Muhammad Mamdani, Mohammed Al‑Omran, Naif M Alotaibi

“…The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. …”
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Gaia-X-Med: Experiences with Building Dataspaces for Medical Applications by Bennet Gerlach, Hannes Hesse, Stefan Fischer, Martin Leucker

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Leveraging Open-Source Large Language Models for Data Augmentation in Hospital Staff Surveys: Mixed Methods Study by Carl Ehrett, Sudeep Hegde, Kwame Andre, Dixizi Liu, Timothy Wilson

“…ConclusionsThe study demonstrates the value of data augmentation with open-source LLMs, highlights the importance of privacy and ethical considerations when using LLMs, and suggests future directions for research in this field.…”
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Reversible Adversarial Examples with Minimalist Evolution for Recognition Control in Computer Vision by Shilong Yang, Lu Leng, Ching-Chun Chang, Chin-Chen Chang

“…As artificial intelligence increasingly automates the recognition and analysis of visual content, it poses significant risks to privacy, security, and autonomy. Computer vision systems can surveil and exploit data without consent. …”
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AI-based treatment of psychological conditions: the potential use, benefits and drawbacks by Michael Baber, Barbara Baker

“…This suggests that translating small-scale, short-term trials into effective large-scale, longer-term real-world applications may be a particular challenge. While the use of AI in mental healthcare appears to have potential, its use also raises important ethical and privacy concerns, potential risk of bias, and the risk of unintended consequences such as over-diagnosis or unnecessary treatment of normal emotional experiences. …”
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Horizontal federated learning and assessment of Cox models by Frank Westers, Sam Leder, Lucia Tealdi

“…However, training an accurate model requires access to a sufficiently large dataset, which is often challenging due to data fragmentation. A potential solution is to combine data from multiple medical institutions, but privacy constraints typically prevent direct data sharing. …”
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Overcoming Data Scarcity in Roadside Thermal Imagery: A New Dataset and Weakly Supervised Incremental Learning Framework by Arnd Pettirsch, Alvaro Garcia-Hernandez

“…Roadside camera systems are commonly used for traffic data collection, yet conventional optical systems are limited by poor performance in varying weather and light conditions and are often restricted by data privacy regulations. …”
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Analysis of Thesis Studies on Ethics in Social, Health and Educational Sciences: A Systematic Review by Merve Kıpçak, Adnan Çetin

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A secure IoT-edge architecture with data-driven AI techniques for early detection of cyber threats in healthcare by Mamta Kumari, Mahendra Gaikwad, Salim A. Chavan

“…Abstract The latest developments in the Internet of Medical Things (IoMT) have transformed healthcare, allowing for the easy transmission of sensitive data across networked medical equipment. However, this interconnection has produced a fertile environment for cybersecurity threats, necessitating the use of sophisticated intrusion detection systems. …”
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Federated two-stage transformer-based network for intrusion detection in non-IID data of controller area networks by Yuan Zhang, Jiaru Song, Yongxiong Sun, Zhanheng Gao, Zhe Hu, Minghui Sun

“…At the same time, the data from individual providers often exhibit imbalance and a scarcity of anomalous samples, leading to suboptimal model performance when using a single dataset. …”
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Building and Sustaining Public Trust in Health Data Sharing for Musculoskeletal Research: Semistructured Interview and Focus Group Study by Zainab K Yusuf, William G Dixon, Charlotte Sharp, Louise Cook, Søren Holm, Caroline Sanders

“… BackgroundAlthough many people are supportive of their deidentified health care data being used for research, concerns about privacy, safety, and security of health care data remain. …”
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DIGITAL DATA-DRIVEN APPROACHES TO MONITOR AND FOSTER WELL-BEING AMONG EXCHANGE STUDENTS DURING DISRUPTIVE EVENTS by Anastasiia Volkova, Nadiia Dets, Olshevska Olga, Yulia Tsarynna, Liudmyla Fihurska

“…Special attention is given to ethical data use, privacy protection, and accessibility of technologies within educational contexts. …”
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The Use of AI in Elementary School Learning: A Systematic Literature Review by Natalina Purba, Desti Pujiati, Partohap Saut Raja Sihombing, Hendra Simanjuntak, Desi Sijabat

“…However, the study also highlights challenges such as inadequate infrastructure, teacher competence, and ethical concerns regarding data privacy and algorithmic bias. Addressing these challenges requires investment in infrastructure, ongoing teacher training, and clear ethical guidelines to ensure equitable access to AI-driven learning. …”
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