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The rapid progress of the Internet has significantly boosted information exchange and aggregation. However, it has also heightened concerns regarding privacy issues such as personal data leakage and misuse. Previous studies have examined how demographic variables and personality traits affect Internet privacy concerns. Nevertheless, these factors are multi-dimensional and complex. Interpersonal factors and psychological characteristics such as social anxiety and privacy protection self-efficacy also deserve consideration. A structured questionnaire was utilized to survey 824 Chinese university students. Structural equation modeling was employed to explore the mediating roles of social anxiety and privacy-preserving self-efficacy in the relationship between personality traits and privacy concerns. Conscientiousness, privacy-preserving self-efficacy, and social anxiety positively forecast Internet privacy concerns among university students. Extroversion, agreeableness, and openness have significant negative impacts on privacy concerns. Social anxiety and privacy-preserving self-efficacy act as chain mediators in the relationship between agreeableness and privacy concerns, as well as between conscientiousness and privacy concerns. The findings offer new perspectives on the underlying mechanisms of Internet privacy issues and emphasize how offline activities influence Internet behavior. A comprehensive and multifaceted approach is required to address Internet privacy concerns among university students.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089604 | PMC |
http://dx.doi.org/10.1038/s41598-025-01737-1 | DOI Listing |
PLOS Digit Health
September 2025
Laerdal Medical AS, Stavanger, Norway.
Accurate observations at birth and during newborn resuscitation are fundamental for quality improvement initiatives and research. However, manual data collection methods often lack consistency and objectivity, are not scalable, and may raise privacy concerns. The NewbornTime project aims to develop an AI system that generates accurate timelines from birth and newborn resuscitation events by automated video recording and processing, providing a source of objective and consistent data.
View Article and Find Full Text PDFJ Vis Exp
August 2025
School of Cyberspace Security, Zhengzhou University.
In the context of the rapid development of large language models (LLMs), contrastive learning has become widely adopted due to its ability to bypass costly data annotation by leveraging vast amounts of network data for model training. However, this widespread use raises significant concerns regarding data privacy protection. Unlearnable Examples (UEs), a technique that disrupts model learning by perturbing data, effectively prevents unauthorized models from misusing sensitive data.
View Article and Find Full Text PDFFront Genet
August 2025
Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN, United States.
Research carried out by Vanderbilt University's and Medical Center's federally-funded transdisciplinary, highly interactive GetPreCiSe Center in Excellence for ELSI research on genomic privacy-involving over 40 scholars across computer and social sciences, law, and the humanities-is summarized by dividing the work into five categories: (1) the nature of risks posed by collection of genetic data; (2) legal and scientific methods of minimizing those risks; (3) methods of safely increasing the scope of genetic databases; (4) public perceptions of genetic privacy; and (5) cultural depictions of genetic privacy. While this research shows that the risk of unauthorized re-identification is often over-stated, it also identifies possible ways privacy can be compromised. Several technical and legal methods for reducing privacy risks are described, most of which focus not on collection of the data, but rather on regulating data security, access, and use once it is collected.
View Article and Find Full Text PDFFront Digit Health
August 2025
Architecture Laboratory, Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan.
Background: Microwave Doppler sensors, capable of detecting minute physiological movements, enable the measurement of biometric information, such as walking patterns, heart rate, and respiration. Unlike fingerprint and facial recognition systems, they offer authentication without physical contact or privacy concerns. This study focuses on non-contact seismocardiography using microwave Doppler sensors and aims to apply this technology for biometric authentication.
View Article and Find Full Text PDFSAGE Open Nurs
September 2025
Nursing College, Palestine Polytechnic University, Hebron, Palestine.
Background: Artificial intelligence (AI) is rapidly transforming healthcare education and practice, making it essential for nursing and health sciences students to develop relevant competencies. However, their preparedness to engage meaningfully with AI in academic and clinical environments remains uncertain.
Objectives: This systematic review synthesizes global evidence on the knowledge, attitudes, practices, and barriers (KAPB) related to AI among students in nursing, medicine, pharmacy, and allied health disciplines.