Search Results (34)

As data becomes a core driver of modern business innovation, mobile applications increasingly collect and process users’ personal information, posing significant challenges to the effectiveness of informed consent and the legitimacy of user authorization. Existing research on privacy informed consent mechanisms has predominantly focused on privacy policy texts and normative legal discussions, often overlooking a critical touchpoint—the launch-time privacy pop-up window. Moreover, empirical investigations from the user’s perspective remain limited. To address these issues, this study employs a two-stage approach combining compliance audit and grounded theory. The preliminary audit of 21 mobile apps assesses the compliance of privacy pop-ups, and the formal study uses thematic analysis of interviews with 19 participants to construct a dual-path explanatory framework. Key findings reveal that: (1) while the reviewed apps partially safeguarded users’ right to be informed, compliance deficiencies still persist; (2) trust and privacy fatigue emerge as dual motivations driving user consent. Trust plays a critical role in amplifying the impact of positive messages within privacy pop-ups by enhancing the consistency among users’ cognition, affect, and behavior, thereby reducing resistance to privacy consent and improving the effectiveness of the current informed consent framework. Conversely, privacy fatigue increases the inconsistency among these factors, undermining consent effectiveness and exacerbating the challenges associated with informed consent. This study offers a user-centered framework to explain the dynamics of informed consent in mobile privacy pop-ups and provides actionable insights for regulators, developers, and privacy advocates seeking to enhance transparency and user autonomy. Full article

The rapid evolution of the transport industry requires a deep understanding of user preferences for emerging mobility solutions, particularly carsharing (CS) and autonomous vehicles (AVs). This study employs machine learning techniques to model transport mode choice, with a focus on traffic safety perceptions of people towards CS and privately shared autonomous vehicles (PSAVs). A stated preference (SP) survey is conducted to collect data on travel behavior, incorporating key attributes such as trip time, trip cost, waiting and walking time, privacy, cybersecurity, and surveillance concerns. Sociodemographic factors, such as income, gender, education, employment status, and trip purpose, are also examined. Three gradient boosting models—CatBoost, XGBoost, and LightGBM are applied to classify user choices. The performance of models is evaluated using accuracy, precision, and F1-score. The XGBoost demonstrates the highest accuracy (77.174%) and effectively captures the complexity of mode choice behavior. The results indicate that CS users are easily classified, while PSAV users present greater classification challenges due to variations in safety perceptions and technological acceptance. From a traffic safety perspective, the results emphasize that companionship, comfort, privacy, cybersecurity, safety in using CS and PSAVs, and surveillance significantly influence CS and PSAV acceptance, which leads to the importance of trust in adopting AVs. The findings suggest that ensuring public trust occurs through robust safety regulations and transparent data security policies. Furthermore, the envisaged benefits of shared autonomous mobility are alleviating congestion and promoting sustainability. Full article

The rapid development of autonomous driving technology has been a key driver for the emergence of driverless delivery vehicles. To promote wider adoption, it is essential to address consumers’ concerns about safety and reliability, leverage psychological factors, and implement supportive policies that encourage technology adoption while ensuring public safety and privacy. Therefore, it is necessary to explain and predict consumers’ behavior and intention to adopt driverless delivery vehicles. To this end, this study extends the Technology Acceptance Model (TAM) to include technological complexity and perceived trust. This study evaluates the model by applying necessary condition analysis (NCA) and partial least squares structural equation modeling (PLS-SEM) to analyze data from 579 respondents from Jiangsu Province, China. This study explores the sustainability implications of autonomous delivery vehicles, highlighting their potential to reduce environmental impact and promote a more sustainable transportation system. The outcomes indicate that perceived ease of use (PEU), attitude, perceived trust, technological complexity (TECOM), and perceived usefulness (PU) are significant determinants and necessary conditions of consumers’ intention to adopt driverless delivery vehicles. Perceived trust and TECOM had a significant and indirect influence on consumers’ intention to adopt driverless delivery vehicles via PU and PEU. Perceived trust and technological complexity had a substantial impact on consumers’ adoption intention of driverless delivery vehicles. The study recommends that managers work closely with regulators to ensure their technologies meet all local standards and regulations. It also recommends its potential to reduce carbon emissions, improve energy efficiency, and contribute to a more sustainable transportation system. Full article

The integration of artificial intelligence (AI) in special education has the potential to transform learning experiences and improve outcomes for students with disabilities. This systematic literature review examines the application of AI technologies in special education, focusing on personalized learning, cognitive and behavioral interventions, communication, emotional support, and physical independence. Through an analysis of 15 studies conducted between 2019 and 2024, the review synthesizes evidence on the effectiveness of AI tools, including intelligent tutoring systems, adaptive learning platforms, assistive communication devices, and robotic aids. The findings suggest that AI-driven technologies significantly enhance students’ academic performance, communication skills, emotional regulation, and physical mobility by providing tailored interventions that address individual needs. This review also highlights several challenges, including limited access to AI technologies in low-resource settings, the need for more comprehensive teacher training, and ethical concerns related to data privacy and algorithmic bias. Additionally, the geographic focus of the current research is primarily on developed countries, overlooking the specific challenges of implementing AI in resource-constrained environments. This review emphasizes the need for more diverse and ethical research to fully realize the potential of AI in supporting students with disabilities and promoting inclusive education. Full article

With the rapid advancement of intelligent transportation systems (ITSs), behavior detection within the Internet of Vehicles (IoVs) has become increasingly critical for maintaining system security and operational stability. However, existing detection approaches face significant challenges related to data privacy, node trustworthiness, and system transparency. To address these limitations, this study proposes a blockchain-driven federated learning framework for anomaly detection in IoV environments. A reputation evaluation mechanism is introduced to quantitatively assess the credibility and contribution of connected and autonomous vehicles (CAVs), thereby enabling more effective node management and incentive regulation. In addition, a multi-level model aggregation strategy based on dynamic vehicle selection is developed to integrate local models efficiently, with the optimal global model securely recorded on the blockchain to ensure immutability and traceability. Furthermore, a reputation-based prepaid reward mechanism is designed to improve resource utilization, enhance participant loyalty, and strengthen overall system resilience. Experimental results confirm that the proposed framework achieves high anomaly detection accuracy and selects participating nodes with up to 99% reliability, thereby validating its effectiveness and practicality for deployment in real-world IoV scenarios. Full article

Genomic and epigenomic instability are defining features of cancer, driving tumor progression, heterogeneity, and therapeutic resistance. Central to this process are epigenetic echoes, persistent and dynamic modifications in DNA methylation, histone modifications, non-coding RNA regulation, and chromatin remodeling that mirror underlying genomic chaos and actively influence cancer cell behavior. This review delves into the complex relationship between genomic instability and these epigenetic echoes, illustrating how they collectively shape the cancer genome, affect DNA repair mechanisms, and contribute to tumor evolution. However, the dynamic, context-dependent nature of epigenetic changes presents scientific and ethical challenges, particularly concerning privacy and clinical applicability. Focusing on lung cancer, we examine how specific epigenetic patterns function as biomarkers for distinguishing cancer subtypes and monitoring disease progression and relapse. Full article

The rapid advancement and integration of Artificial Intelligence (AI) in Internet of Things (IoT) technologies have raised significant concerns regarding privacy information disclosure. As AI-enabled IoT devices collect, process, and share vast amounts of personal data, it is crucial to understand the current state of research on this topic and identify areas for future investigation. This research systematically analyzed 38 peer-reviewed articles on privacy information disclosure in the AI-enabled IoT context. The analysis yielded pivotal themes pertinent to information disclosure in the IoT realm, encompassing facets such as consumer IoT adoption, personalized service, the commodification of information, external threats, vulnerability, innovation, regulation, behavioral patterns, trust, demographic considerations, user satisfaction, strategic marketing plans, and institutional reputation. This paper posits a combined summary research framework explaining user-centric information disclosure behavior in the IoT sphere in light of these disclosures. The insights presented cater to diverse stakeholders, including researchers, policymakers, and businesses, aiming for optimized AI-integrated IoT engagement while prioritizing privacy. Full article

Although digital hoarding behavior does not directly affect physical space, with the popularization of cloud storage services, its impact on energy consumption has become increasingly significant, posing a challenge to environmental sustainability. This study focuses on the factors influencing consumer digital hoarding behavior on e-commerce platforms, aiming to provide management decision-making references for e-commerce enterprises to deal with consumer digital hoarding phenomena and improve transaction effectiveness. Based on the Motivation–Opportunity–Ability (MOA) Theory and through the Adversarial Interpretive Structure Modeling Method (AISM), this study systematically identifies and analyzes the influencing factors. The findings reveal that emotional attachment, burnout, and fear of missing out are the main motivational factors directly affecting consumer digital hoarding behavior, with strong interconnections between these factors. Perceived usefulness and platform interaction design are significant opportunity factors, indirectly affecting digital hoarding behavior by improving user experience and satisfaction. E-commerce platform convenience, anticipated ownership, perceived economic value, emotional regulation ability, auxiliary shopping decision-making, perceived behavioral control, and information organization ability are the foundational and intermediate factors. The research results emphasize the importance of understanding consumer digital hoarding behavior in the context of sustainable development. This is not only conducive to optimizing the shopping cart function and data management strategy of e-commerce platforms and improving transaction conversion rates but also provides a reference for policymakers to formulate data management and privacy protection policies. Full article

Federated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. However, achieving a delicate privacy preservation–model convergence balance remains a major problem. Understanding how different hyperparameters affect this balance is crucial for optimizing FL systems. This article examines the impact of various hyperparameters, like the privacy budget (ϵ), clipping norm (C), and the number of randomly chosen clients (K) per communication round. Through a comprehensive set of experiments, we compare training scenarios under both independent and identically distributed (IID) and non-independent and identically distributed (Non-IID) data settings. Our findings reveal that the combination of ϵ and C significantly influences the global noise variance, affecting the model’s performance in both IID and Non-IID scenarios. Stricter privacy conditions lead to fluctuating non-converging loss behavior, particularly in Non-IID settings. We consider the number of clients (K) and its impact on the loss fluctuations and the convergence improvement, particularly under strict privacy measures. Thus, Non-IID settings are more responsive to stricter privacy regulations; yet, with a higher client interaction volume, they also can offer better convergence. Collectively, knowledge of the privacy-preserving approach in FL has been extended and useful suggestions towards an ideal privacy–convergence balance were achieved. Full article

Background: In the transition to a professional learning environment, healthcare professionals in their first year of specialized postgraduate clinical training (known as residents in Spain) are suddenly required to handle confidential information with little or no prior training in the safe and appropriate use of digital media with respect to confidentiality issues. The aims of this study were: (1) to explore the usefulness of an advanced clinical simulation program for educating residents from different healthcare disciplines about confidentiality and the dissemination of clinical data or patient images; (2) to explore the use of social networks in healthcare settings; and (3) to explore participants’ knowledge and attitudes on current regulations regarding confidentiality, image dissemination, and the use of social networks; Methods: This was a cross-sectional study. Data were collected from all 49 first-year residents of different health professions at a Spanish hospital between June and August 2022. High-fidelity clinical simulation sessions designed to address confidentiality and health information dissemination issues in hospital settings, including the use of social networks, were developed and implemented. Data were assessed using a 12-item ad hoc questionnaire on confidentiality and the use of social media in the healthcare setting. Descriptive of general data and chi-square test or Fisher’s exact test were performed using the SPSS 25.0 software; Results: All the participants reported using the messaging application WhatsApp regularly during their working day. A total of 20.4% of the participants stated that they had taken photos of clinical data (radiographs, analyses, etc.) without permission, with 40.8% claiming that they were unaware of the legal consequences of improper access to clinical records. After the course, the participants reported intending to modify their behavior when sharing patient data without their consent and with respect to how patients are informed; Conclusions: The use of advanced simulation in the training of interprofessional teams of residents is as an effective tool for initiating attitudinal change and increasing knowledge related to patient privacy and confidentiality. Further follow-up studies are needed to see how these attitudes are incorporated into clinical practice. Full article

Background/Objectives: Social media has become a significant part of daily life, with platforms like Facebook and WhatsApp dominating usage. The COVID-19 pandemic further increased social media activity, including within the orthopedic community due to restrictions on physical gatherings. Despite the benefits of instant access to educational resources and interaction with experts, the lack of regulated editorial oversight on social media raises concerns about misinformation and privacy. This study aimed to evaluate the role of social media in orthopedic and trauma surgery education, focusing on platform use, user behavior, and engagement with educational content. Methods: A web-based survey was distributed to 912 residents and 728 medical students from the German-speaking Association for Arthroscopy and Joint Surgery (AGA) between June and July 2022. The questionnaire included 21 items covering demographics, platform use, activity patterns, engagement with educational content, and concerns about privacy. Results: Of the 339 respondents (129 medical students), 87% reported daily social media use, primarily via smartphones (93%). The most commonly used platforms were WhatsApp (84%), Instagram (68%), and YouTube (54%). About 26% of the content consumed was related to orthopedics or trauma surgery. While 70% engaged with specialist content by liking, commenting, or sharing, only 32% posted their own content. Additionally, 77% followed healthcare professionals or institutions, and 65% benefited from case presentations with images. Notably, 15% observed content that could violate patient privacy. Conclusions: Orthopedic residents and students are high-volume social media users but engage more passively with professional content. While most value educational material, concerns about privacy violations and inappropriate posts remain prevalent. Full article

This paper examines the impact of stringent regulations on personal data protection on customer perception of data security and online shopping behavior. In the context of the rapidly expanding e-commerce landscape, ensuring the security of personal data is a complex and crucial task. The study of several legal frameworks, including Malaysia’s compliance with EU regulations and Indonesia’s Personal Data Protection Law, provides valuable insights into consumer data protection. The challenges of balancing data safeguarding and unrestricted movement and tackling misuse by external entities are significant and require careful consideration. This research elucidates the pivotal role of trust in e-commerce environments and the deployment of innovative e-commerce models designed to minimize personal data sharing. By integrating advanced privacy-enhancing technologies and adhering to stringent regulatory standards such as the GDPR, this study demonstrates effective strategies for robust data protection. The paper contributes to the academic discourse by providing a comprehensive framework that synergizes legal, technological, and procedural elements to fortify data security and enhance consumer trust in digital marketplaces. This approach aligns with international data protection standards and offers a pragmatic blueprint for achieving sustainable data security in e-commerce. Full article

Recent advances in artificial intelligence combined with behavioral sciences have led to the development of cutting-edge tools for recognizing human emotions based on text, video, audio, and physiological data. However, these data sources are expensive, intrusive, and regulated, unlike plants, which have been shown to be sensitive to human steps and sounds. A methodology to use plants as human emotion detectors is proposed. Electrical signals from plants were tracked and labeled based on video data. The labeled data were then used for classification., and the MLP, biLSTM, MFCC-CNN, MFCC-ResNet, Random Forest, 1-Dimensional CNN, and biLSTM (without windowing) models were set using a grid search algorithm with cross-validation. Finally, the best-parameterized models were trained and used on the test set for classification. The performance of this methodology was measured via a case study with 54 participants who were watching an emotionally charged video; as ground truth, their facial emotions were simultaneously measured using facial emotion analysis. The Random Forest model shows the best performance, particularly in recognizing high-arousal emotions, achieving an overall weighted accuracy of 55.2% and demonstrating high weighted recall in emotions such as fear (61.0%) and happiness (60.4%). The MFCC-ResNet model offers decently balanced results, with $Accurac{y}_{MFCC-ResNet}=0.318$ and $Recal{l}_{MFCC-ResNet}=0.324$. Regarding the MFCC-ResNet model, fear and anger were recognized with 75% and 50% recall, respectively. Thus, using plants as an emotion recognition tool seems worth investigating, addressing both cost and privacy concerns. Full article

In the contemporary era, smart buildings, characterized by their integration of advanced technologies to enhance energy efficiency and user experience, are becoming increasingly prevalent. While these advancements offer notable benefits in terms of operational efficiency and sustainability, they concurrently introduce a myriad of privacy concerns. This review article delves into the multifaceted realm of privacy issues associated with energy-efficient smart buildings. We commence by elucidating the potential risks emanating from data collection, storage, and analysis, highlighting the vulnerability of the personal and behavioral information of inhabitants. The article then transitions into discussing the rights of occupants, emphasizing the necessity for informed consent and the ability to opt-out of invasive data collection practices. Lastly, we provide an overview of existing regulations governing the intersection of smart buildings and privacy. We evaluate their effectiveness and present gaps that necessitate further legislative action. By offering a holistic perspective on the topic, this review underscores the pressing need to strike a balance between harnessing the benefits of technology in smart buildings and safeguarding the privacy of their occupants. Full article

To alleviate traffic congestion, it is necessary to effectively manage traffic bottlenecks. In existing research, travel demand prediction for traffic bottlenecks is based on travel behavior assumptions, and prediction accuracy is low in practice. Thus, the effect of traffic bottleneck management strategies cannot be guaranteed. Management strategies are often mandatory, leading to problems such as unfairness and low social acceptance. To address such issues, this paper proposes managing traffic bottlenecks based on shared travel plans. To solve the information security and privacy problems caused by travel plan sharing and achieve information transparency, travel plans are shared and regulated by blockchain technology. To optimize the operation level of traffic bottlenecks, travel plan regulation models under scenarios where all/some travelers share travel plans are proposed and formulated as linear programming models, and these models are integrated into the blockchain with smart contract technology. Furthermore, travel plan regulation models are tested and verified using traffic flow data from the Su-Tong Yangtze River Highway Bridge, China. The results indicate that the proposed travel plan regulation models are effective for alleviating traffic congestion. The vehicle transfer rate and total delay rate increase as the degree of total demand increases; the vehicle transfer rate increases as the length of the time interval decreases; and the vehicle transfer rate and total delay rate increase as the number of vehicles not sharing their travel plans increases. By using the model and method proposed in this paper, the sustainability of urban economy, society, and environment can be promoted. However, there are many practical situations that have not been considered in this paper, such as multiple entry and exit bottlenecks, multiple travel modes, and other control strategies. In addition, this paper considers only one bottleneck rather than road networks because of the throughput limitations of blockchain technology. Full article