Search Results (436)

Privacy and security is very critical for mobile users and in-depth research into the area highlights a need for more scientific literature on the perception and challenges of end users to better align the design of privacy and security controls with user expectations. In this paper, we have explored the perceptions of the usability of privacy and security settings in mobile applications from mobile users in Saudi Arabia. The findings highlight that gender, age, and education level of users do not have any positive correlation with the privacy and security usability perceptions of mobile users. On the other hand, user concerns about privacy and security and the trustworthiness levels of end users regarding mobile phone privacy and security have a positive impact on end users’ perception of privacy and security usability. Furthermore, privacy usability perception has a positive impact on users’ feelings about their control over the privacy and security of their mobile phones. Based on the results of this empirical study, we propose that user-centric design of privacy and security controls, transparent data handling policies, periodic data management status preview and validation by end users, user education guidelines, strict governmental policies, and automated security settings recommendations can enhance the usability of the privacy and security of mobile phone applications. Our study did not take the geographical location of respondents into account, nor were the respondents balanced based on age and gender. In future work, these weaknesses need to be taken into account, and more qualitative studies can help to extract design guidelines for usable and secure mobile applications. Full article

This paper addresses the challenge of preserving user privacy in location-based services (LBSs) by proposing a novel, complementary approach to existing privacy-preserving techniques such as k-anonymity and l-diversity. Our approach implements collaborative caching strategies within a mobile ad hoc network (MANET), exploiting the geographic of location-based queries (LBQs) to reduce data exposure to untrusted LBS servers. Unlike existing approaches that rely on centralized servers or stationary infrastructure, our solution facilitates direct data exchange between users’ devices, providing an additional layer of privacy protection. We introduce a new privacy entropy-based metric called accumulated privacy loss (APL) to quantify the privacy loss incurred when accessing either the LBS or our proposed system. Our approach implements a two-tier caching strategy: local caching maintained by each user and neighbor caching based on proximity. This strategy not only reduces the number of queries to the LBS server but also significantly enhances user privacy by minimizing the exposure of location data to centralized entities. Empirical results demonstrate that while our collaborative caching system incurs some communication costs, it significantly mitigates redundant data among user caches and reduces the need to access potentially privacy-compromising LBS servers. Our findings show a 40% reduction in LBS queries, a 64% decrease in data redundancy within cells, and a 31% reduction in accumulated privacy loss compared to baseline methods. In addition, we analyze the impact of data obsolescence on cache performance and privacy loss, proposing mechanisms for maintaining the relevance and accuracy of cached data. This work contributes to the field of privacy-preserving LBSs by providing a decentralized, user-centric approach that improves both cache redundancy and privacy protection, particularly in scenarios where central infrastructure is unreachable or untrusted. Full article

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing. Full article

Federated learning (FL) is creating a paradigm shift in machine learning by directing the focus of model training to where the data actually exist. Instead of drawing all data into a central location, which raises concerns about privacy, costs, and delays, FL allows learning to take place directly on the device, keeping the data safe and minimizing the need for transfer. This approach is especially important in areas like healthcare, where protecting patient privacy is critical, and in industrial IoT settings, where moving large numbers of data is not practical. What makes FL even more compelling is its ability to reduce the bias that can occur when all data are centralized, leading to fairer and more inclusive machine learning outcomes. However, it is not without its challenges—particularly with regard to keeping the models secure from attacks. Nonetheless, the potential benefits are clear: FL can lower the costs associated with data storage and processing, while also helping organizations to meet strict privacy regulations like GDPR. As edge computing continues to grow, FL’s decentralized approach could play a key role in shaping how we handle data in the future, moving toward a more privacy-conscious world. This study identifies ongoing challenges in ensuring model security against adversarial attacks, pointing to the need for further research in this area. Full article

In this study, we aimed to integrate a genetic repository with an existing longitudinal national burn database. We set out two primary objectives, namely (1) to develop standard operating procedures for genetic sample collection and storage, DNA isolation, and data integration into an existing multicenter database; and (2) to demonstrate the feasibility of correlating genetic variation to functional outcomes in a pilot study, using the catechol-O-methyltransferase (COMT) gene. Dubbed the worrier/warrior gene, COMT variants have been associated with varying phenotypes of post-traumatic stress, wellbeing, and resilience. Between August 2018 and July 2020, COMT variants were identified for 111 participants from three sites and correlated with their outcome data. We found no association between COMT variants and functional outcomes, likely due to the inadequate sample size. We also asked all potential participants why they consented to or refused genetic analysis. A thematic analysis of responses revealed altruism and personal interest/enthusiasm in the study as top reasons for consenting. Privacy concerns were the most common reason for refusal. In conclusion, we successfully developed standard operating procedures for genetic sample collection and storage, DNA isolation, and data integration into an existing database, and we demonstrated the feasibility of conducting a multicenter collaborative study using a centralized lab location. Full article

(1) Background: Migrant and refugee youth’s (MRY’s) sexual and reproductive health and rights (SRHRs) are a global health issue. MRY encounter adverse SRH experiences due to limited access to and knowledge of SRHRs services. Using a socioecological framework, this study examined the barriers affecting MRY’s SRHRs. (2) Methods: A cross-sectional study utilising a participatory action research design was used. A total of 87 MRY (ages 16–26, from 20 cultural groups within Greater Western Sydney, Australia) completed seventeen focus group discussions on their SRHRs experiences. The focus groups were co-facilitated by youth project liaisons to ensure their authenticity and validity. The data were analysed thematically and interpreted using socioecological theory. (3) Results: The findings identified socioecological barriers, a lack of awareness about and access to services, and sociocultural dissonance as leading to the under-implementation of SRHRs services. These barriers included cultural disconnects, language barriers, remote service locations, intergenerational cultural conflicts, and ineffective SRHRs services. The key themes identified included traditional and institutional stigma, lack of SRH education, reliance on social media for SRH information, and privacy concerns. (4) Conclusions: There is a limited consideration of MRY’s SRHRs and the impact of intergenerational discordance and stigma on MRY’s rights. The findings suggest the necessity for a collaborative SRHRs strategy and policy design that empowers MRY’s agency across multicultural contexts. Full article

In 5G vehicular networks, two key challenges have become apparent, including end-to-end delay minimization and data privacy. Learning-based approaches have been used to alleviate these, either by predicting delay or protecting privacy. Traditional approaches train machine learning models on local devices or cloud servers, each with their own trade-offs. While pure-federated learning protects privacy, it sacrifices delay prediction performance. In contrast, centralized training improves delay prediction but violates privacy. Existing studies in the literature overlook the effect of training location on delay prediction and data privacy. To address both issues, we propose a novel genetic algorithm optimized federated learning (GAoFL) approach in which end-to-end delay prediction and data privacy are jointly considered to obtain an optimal solution. For this purpose, we analytically define a novel end-to-end delay formula and data privacy metrics. Accordingly, a novel fitness function is formulated to optimize both the location of training model and data privacy. In conclusion, according to the evaluation results, it can be advocated that the outcomes of the study highlight that training location significantly affects privacy and performance. Moreover, it can be claimed that the proposed GAoFL improves data privacy compared to centralized learning while achieving better delay prediction than other federated methods, offering a valuable solution for 5G vehicular computing. Full article

As digital technology advances, the proliferation of connected devices poses significant challenges and opportunities in mobile crowdsourcing and edge computing. This narrative review focuses on the need for privacy protection in these fields, emphasizing the increasing importance of data security in a data-driven world. Through an analysis of contemporary academic literature, this review provides an understanding of the current trends and privacy concerns in mobile crowdsourcing and edge computing. We present insights and highlight advancements in privacy-preserving techniques, addressing identity, data, and location privacy. This review also discusses the potential directions that can be useful resources for researchers, industry professionals, and policymakers. Full article

The rapid advancement and adoption of electric vehicles (EVs) necessitate innovative solutions to address integration challenges in modern charging infrastructure. Dynamic wireless charging (DWC) is an innovative solution for powering electric vehicles (EVs) using multiple magnetic transmitters installed beneath the road and a receiver located on the underside of the EV. Dynamic charging offers a solution to the issue of range anxiety by allowing EVs to charge while in motion, thereby reducing the need for frequent stops. This manuscript reviews several pivotal areas critical to the future of EV DWC technology such as authentication techniques, blockchain applications, driver identification systems, economic aspects, and emerging communication technologies. Ensuring secure access to this charging infrastructure requires fast, lightweight authentication systems. Similarly, blockchain technology plays a critical role in enhancing the Internet of Vehicles (IoV) architecture by decentralizing and securing vehicular networks, thus improving privacy, security, and efficiency. Driver identification systems, crucial for EV safety and comfort, are analyzed. Additionally, the economic feasibility and impact of DWC are evaluated, providing essential insights into its potential effects on the EV ecosystem. The paper also emphasizes the need for quick and lightweight authentication systems to ensure secure access to DWC infrastructure and discusses how blockchain technology enhances the efficiency, security, and privacy of IoV networks. The importance of driver identification systems for comfort and safety is evaluated, and an economic study confirms the viability and potential benefits of DWC for the EV ecosystem. Full article

The mining industry is undergoing a transformation driven by the adoption of Industry 4.0 technologies, implementing autonomous trucks, drones, positions systems, and similar technologies. This article, drawing on experiences and observations from several studies conducted in the mining industry, explores the impact of these technologies on mining work. It identifies eight key potential changes in working conditions. Firstly, routine and dangerous tasks are increasingly automated, reducing physical strain but potentially leading to job displacement and increased maintenance demands. Secondly, operators and managers are shifting toward handling disturbances and training algorithms, as AI takes over decision-making processes. Thirdly, managers are responsible for more capital with fewer people, potentially altering managerial roles and spans of control. Fourthly, the global connectivity of operations makes the world both larger and smaller, with a universal language blurring boundaries. Fifthly, work becomes location-independent, allowing for remote operation and management. Sixthly, the distinction between work and private life blurs, with increased availability expected from operators and managers. Seventhly, technology expands human senses, providing real-time data and situational awareness. Eighthly and lastly, the pervasive collection and retention of data create a scenario where one’s history is inescapable, raising concerns about data ownership and privacy. These changes necessitate a strategic response from the mining industry to ensure socially sustainable technology development and to attract a future workforce. Full article

Geomasking is a set of techniques that introduces noise or intentional errors into geospatial data to minimize the risk of identifying exact location information related to individuals while preserving the utility of the data to a controlled extent. It protects the geoprivacy of the data contributor and mitigates potential harm from data breaches while promoting safer data sharing. The development of digital health technologies and the extensive use of individual geospatial data in health studies have raised concerns about geoprivacy. The individual tracking data and health information, if accessed by unauthorized parties, may lead to privacy invasions, criminal activities, and discrimination. These risks underscore the importance of robust protective measures in the collection, management, and sharing of sensitive data. Geomasking techniques have been developed to safeguard geoprivacy in geospatial health data, addressing the risks and challenges associated with data sharing. This entry paper discusses the importance of geoprivacy in geospatial health data and introduces various kinds of geomasking methods and their applications in balancing the protection of individual privacy with the need for data sharing to ensure scientific reproducibility, highlighting the urgent need for more effective geomasking techniques and their applications. Full article

The Internet of Things (IoT) has emerged as a transformative technology with the potential to revolutionize various sectors and industries worldwide. Despite its global significance, the adoption and implementation of IoT technologies in emerging technology ecosystems within the Central African region still need to be studied and explored. This paper presents a case study of the Silicon Mountain technology ecosystem, located in Fako division of the southwest region of Cameroon, focusing on the barriers and challenges to adopting and integrating IoT technologies within this emerging tech ecosystem. Through a survey-based approach, we investigate the factors influencing IoT adoption in the Silicon Mountain tech ecosystem, including technological, economic, social, and regulatory factors. Our study reveals key insights into the current state of IoT adoption, opportunities for growth and innovation, and IoT adoption challenges. Key among the challenges identified for impeding IoT uptake were issues related to standardization and financial resources, labor shortage in the industry, educational and knowledge gaps, market challenges, government policies, security and data privacy concerns, and inadequate power supply. Based on our findings, we provide recommendations for policymakers, industry stakeholders, and academic institutions to promote and facilitate the widespread adoption of IoT technologies in Silicon Mountain and the Central African region at large. Full article

Online ride-hailing services have become a vital component of urban transportation worldwide due to their convenience and flexibility. However, the expansion of their user base has dramatically heightened the risks of user privacy information leakage. Among these risks, the privacy leakage problem caused by the direct correlation between user (driver and passenger) identity information and location-based ride information is of particular concern. This paper proposes a novel privacy protection scheme for ride-hailing services. In this scheme, decentralized identities are employed for user authentication, separating the identity registration service from the ride-hailing platform, thereby preventing the platform from obtaining user privacy data. The scheme also employs a fuzzy matching strategy based on location Points of Interest (POI) and a ciphertext-policy attribute-based hybrid encryption algorithm to hide the user’s precise location and restrict access to location information. Crucially, the scheme achieves the complete decoupling of identity registration services and location-based ride services on the ride-hailing platform, ensuring that users’ real identities and ride data cannot be directly associated, effectively protecting user privacy. Within the decoupled architecture, regulatory authorities are established to handle emergencies within ride-hailing services. Through simulation experiments and security analysis, this scheme is demonstrated to be both feasible and practical, providing a new privacy protection solution for the ride-hailing industry. Full article

In contrast from traditional unmanned aerial vehicle communication via unlicensed spectrum, connecting unmanned aerial vehicles with cellular networks can extend their communication coverage and improve the quality of their service. In addition, the emerging dual-functional radar communication paradigm in cellular systems can better meet the requirements of location-sensitive tasks such as reconnaissance and cargo delivery. Based on the above considerations, in this paper, we study the simultaneous communication and target sensing issue in cellular-connected unmanned aerial vehicle systems. Specifically, we consider a two-cell coordinated system with two base stations, cellular unmanned aerial vehicles, and potential aerial targets. In such systems, the communication security issue of cellular unmanned aerial vehicles regarding eavesdropping on their target is inevitable since the main beam of the transmit waveform needs to point to the direction of the target for achieving a sufficient detection performance. Aiming at protecting the privacy of cellular transmission as well as performing target sensing, we exploit the physical layer security technique with the aid of constructive interference-based precoding. A transmit power minimization problem is formulated with constraints on secure and reliable cellular transmission and a sufficient radar signal-to-interference-plus-noise ratio. By specially designing the transmit beamforming vectors at the base stations, the received signals at the cellular users are located in the decision regions of the transmitted symbols while the targets can only receive wrong symbols. We also compare the performance of the proposed scheme with that of the traditional one without constructive interference. The simulation results show that the proposed constructive interference-based strategy can meet the requirements of simultaneous target sensing and secure communication, and also save transmit power compared with the traditional scheme. Full article

Large Language Models (LLMs) are becoming increasingly adopted in various industries worldwide. In particular, there is emerging research assessing the reliability of LLMs, such as ChatGPT, in performing triaging decisions in emergent settings. A unique aspect of emergency triaging is the process of trauma triaging. This process requires judicious consideration of mechanism of injury, severity of injury, patient stability, logistics of location and type of transport in order to ensure trauma patients have access to appropriate and timely trauma care. Current issues of overtriage and undertriage highlight the potential for the use of LLMs as a complementary tool to assist in more accurate triaging of the trauma patient. Despite this, there remains a gap in the literature surrounding the utility of LLMs in the trauma triaging process. This narrative review explores the current evidence for the potential for implementation of LLMs in trauma triaging. 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. Full article