Trust Mechanism Research Articles

A novel energy efficient QoS secure routing algorithm for WSNs

Quality of Service (QoS) routing protocol is a hot topic in the research field of wireless sensor networks (WSNs). However, the task of identifying an optimal path that simultaneously meets multiple QoS constraints is acknowledged as an NP-hard problem, with its complexity intensifying in proportion to the network’s nodal count. Therefore, a novel heuristic multi-objective trust routing method, the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM), is proposed, aiming to enhance link bandwidth while simultaneously reducing latency, packet loss, and energy consumption. The proposed method incorporates innovative chaos and adaptive operators within the LCASO framework. The chaos operator enhances population diversity, expands the solution space, and accelerates the search process. Meanwhile, the adaptive operator improves convergence, enhances robustness, and effectively prevents stagnation. Additionally, this paper introduces a novel multi-objective QoS routing model that integrates a link trust mechanism, allowing for a more accurate assessment of link trust levels and a precise reflection of the current link status. The effectiveness of LCASO-MTRM is demonstrated through simulation comparisons with the Improved Particle Swarm Optimization (IPSO), Improved Artificial Bee Colony Algorithm (IABC), and Cloned Whale Optimization Algorithm (CWOA). Simulation results demonstrate that LCASO-MTRM significantly reduces energy consumption by 49.53%, latency by 22.56%, and packet loss by 40.21%, while increasing bandwidth by 6.13%, outperforming the other algorithms.

Exploring the mechanism of sustained consumer trust in AI chatbots after service failures: a perspective based on attribution and CASA theories

In recent years, artificial intelligence (AI) technology has been widely employed in brand customer service. However, the inherent limitations of computer-generated natural language content occasionally lead to failures in human-computer interactions, potentially damaging a company’s brand image. Therefore, it is crucial to explore how to maintain consumer trust after AI chatbots fail to provide successful service. This study constructs a model to examine the impact of social interaction cues and anthropomorphic factors on users’ sustained trust by integrating the Computers As Social Actors (CASA) theory with attribution theory. An empirical analysis of 462 survey responses reveals that CASA factors (perceived anthropomorphic characteristics, perceived empathic abilities, and perceived interaction quality) can effectively enhance user trust in AI customer service following interaction failures. This process of sustaining trust is mediated through different attributions of failure. Furthermore, AI anxiety, as a cognitive characteristic of users, not only negatively impacts sustained trust but also significantly moderates the effect of internal attributions on sustained trust. These findings expand the research domain of human-computer interaction and provide insights for the practical development of AI chatbots in communication and customer service fields.

Toward a responsible future: recommendations for AI-enabled clinical decision support.

Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging. This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients. In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process. Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided. AI-enabled Clinical Decision Support (AI-CDS) systems promise to revolutionize healthcare decision-making, necessitating a comprehensive framework for their development, implementation, and regulation that emphasizes trustworthiness, transparency, and safety. This framework encompasses various aspects including model training, explainability, validation, certification, monitoring, and continuous evaluation, while also addressing challenges such as data privacy, fairness, and the need for regulatory oversight to ensure responsible integration of AI into clinical workflow. Achieving responsible AI-CDS systems requires a collective effort from many healthcare stakeholders. This involves implementing robust safety, monitoring, and transparency measures while fostering innovation. Future steps include testing and piloting proposed trust mechanisms, such as safety reporting protocols, and establishing best practice guidelines.

Online interpersonal trust and online altruistic behavior in college students: the chain mediating role of moral identity and online social support.

The prevalence of online altruistic behaviors among the college students has attracted widespread attention. However, the factors influencing this are still unclear. The objective of this study was to explore the relationship and mechanism of online interpersonal trust, moral identity, online social support and online altruistic behavior among college students. The survey was conducted among 986 Chinese college students using the Interpersonal Trust Scale for the Internet, Moral Identity Scale, The Internet Social Support Questionnaire for College Students and The Internet Altruistic Behavior Questionnaire for College Students. Moral identity, online social support, online interpersonal trust and online altruistic behavior were significantly positively correlated (r = 0.09-0.39, p < 0.01). Online social support plays a partial mediating role in the relation between online interpersonal trust and online altruistic behavior, accounting for 33.76% of the total effect, while moral identity and online social support play a chain mediating role in the relation between online interpersonal trust and online altruistic behavior, accounting for 2.23% of the total effect. Online interpersonal trust not only directly affects college students' online altruistic behavior, but also indirectly influences it through moral identity and online social support.

Impact of AI on Customer Experience in Video Streaming Services: A Focus on Personalization and Trust

In the digital entertainment landscape, AI-enhanced video streaming services like Netflix and Hulu significantly shape user experiences through AI-based recommendations. This study examines customer perceptions of these AI-driven suggestions, focusing on factors such as Perceived Interactivity, Service Quality, Commitment, Trust, and Personalization. Using data from a diverse sample in Klang Valley, Malaysia, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA). PLS-SEM models complex relationships to predict customer satisfaction, while NCA identifies essential conditions for desired outcomes in customer experience. Findings reveal that Personalization significantly influences satisfaction (path coefficient = 0.34), with Trust enhancing the impact of Perceived Interactivity (mediation effect size = 0.15). Expectancy confirmation also moderates the relationship between service attributes and satisfaction (β = 0.09). The results highlight the importance of tailored personalization and robust trust mechanisms in AI systems, emphasizing strategies to improve viewer retention and overall satisfaction.

Impact of the destination image and government policy responsiveness of China on Korean travelers’ destination trust and behaviors in the post-pandemic era

The underlying mechanism of the destination trust of Korean travelers and behaviors toward China after the COVID-19 pandemic is explored in the present study, which considers the role of the destination image constituents and perceived government policy responsiveness for tourism recovery. The data was collected from Korean nationals who are currently residing in South Korea and have traveled to China in the last five years. The results revealed that the developed conceptual framework had a satisfactory level of explanatory ability in regards to predicting the travel and recommendation intentions of Korean travelers toward China, which destination trust serves as the key mediator. Moreover, the perception of Korean travelers in regards to Chinese government policy responsiveness for tourism recovery significantly strengthened the effect of image factors on destination trust. The study also discovered that the importance of image factors varies in regards to the formation of trust and destination behaviors. This research provides significant theoretical insights into the post-health crisis tourism recovery, which emphasizes changes in Korean travel patterns in the aftermath of COVID-19 as well as the wider socio-political environment that influences travel destinations. It also presents useful guidance for Chinese tourism stakeholders in regards to implementing effective recovery strategies that are aimed at the lucrative Korean outbound travel market during the post-COVID-19 pandemic period.

Transparency Enhancement and Trust Mechanism Construction of Global Supply Chain Based on Blockchain Technology

Transparency Enhancement and Trust Mechanism Construction of Global Supply Chain Based on Blockchain Technology

Integrating Blockchain technology within an Information Ecosystem

ContextBlockchain-based Information Ecosystems (BBIEs) are a type of information ecosystem in which blockchain technology is used to provide a trust mechanism among parties and to manage shared business logic, breaking the traditional scheme of Information Ecosystems dominated by a leading company and leveraging the decentralization of data management, information flow, and business logic. ObjectiveIn this paper, we propose architecture and technical aspects concerning creating a BBIE, underlining the advantages supplied and the logic decomposition among the business and storage components. MethodThe requirements are derived from the current needs of the collaborative business and the data collected by surveying practitioners. To get these needs we followed the Grounded Theory research approach. We validate our architectural schema against a case study on managing a wine supply chain–involving different companies and supervision authorities. ResultsThe proposed solution integrates blockchain-based applications with the existing information system as a module of the ecosystem, leveraging on the low costs, scalability, and high-level security because of the restricted access to the network. ConclusionWe must go a long way in deepening and refining the possibilities offered by technology in supporting innovative multi-organizational business models. BBIEs can contribute substantially to paving the way in such a direction.

Official media use, political participation, and government trust structure. An empirical study based on the attitude of Chinese netizens

While research has explored the relationship between official media use and hierarchical government trust, there is a lack of studies that delve deeper into the relationship between official media use and government trust structure. Therefore, this study uses data from the 2017-2018 Chinese netizens’ social awareness non-tracking survey and employs rational choice theory and process-based trust mechanisms theory to empirically explore, for the first time, the relationship between official media use and government trust structure, along with its underlying mechanisms. Government trust structure is divided into four categories based on the different levels of trust in the central government and local government: paradoxical government trust, equal distrust, hierarchical government trust and equal trust. Among them, hierarchical government trust can be divided into central-distrust-local-distrust, central-trust-local-distrust, and central-trust-local-trust. The study finds that although hierarchical government trust decreases, it is the dominant form of government trust structure, and central-trust-local-distrust is the dominant form of hierarchical government trust. The transformation of other government trust structures into hierarchical government trust of central-trust-local-trust is associated with official media use, and political participation has a mediating effect in official media use and government trust structure. Our findings not only deepen the understanding of rational choice theory and process-based trust mechanism theory but also expand the comprehensive understanding of government trust structure in authoritarian regimes like China.

Exploring Innovative Structures and Mechanisms in Urban Grassroots Governance: A Network Governance Perspective

This paper examines the "Five Chiefs System" as a grassroots governance model in Chongqing, China, exploring innovative mechanisms in urban grassroots governance from a networked governance perspective. Through a literature review, this study summarizes existing research on innovation in urban grassroots governance, highlighting approaches in technological governance, process re-engineering, and structural research. Building on theories of governance and networked governance, and using the "Five Chiefs System" in Chongqing as a case study, the paper reveals discrepancies between the system and its operational goals as well as the principles of co-governance in practice. Consequently, an analysis of the adaptability of the "Five Chiefs System" and networked governance leads to the exploration of innovative structures and mechanisms for the networked governance of the "Five Chiefs System." This involves developing a decentralized network structure with diverse entities and establishing mechanisms for integration, trust, coordination, and maintenance within networked governance.

A Secure Data Aggregation Algorithm Based on a Trust Mechanism.

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security.

Trust Mechanism-Based Multi-Tier Computing System for Service-Oriented Edge-Cloud Networks

Edge-cloud networks face security threats during data collection, data routing, and service construction, resulting in data tampering, stealing, and communication interruption. Trust mechanism can predict data quality and cooperation probability of nodes before purchasing data or establishing cooperation, so as to select trusted participants for data perception and interaction. However, there are some problems with existing trust methods, such as limited evaluation scope, incomplete trust evidence, and inaccurate evaluation results. To address these issues, a Trust mechanism-based Multi-Tier Computing system (TMTC) is proposed in this paper. Specifically, we propose a two-tier trust evaluation model. At the data collection layer, it conducts trust evaluation on data reporters based on data submission and communication interactions. At the network layer, it evaluates trust of routers through path backtracking verification, multi-service analysis and coincident path analysis. Then, based on evaluation results, a differentiated trust detection is initiated for normal and abnormal nodes. And high-frequency detection tasks are initiated for malicious nodes to improve accuracy, sparse detection tasks are initiated for normal nodes to reduce costs. Finally, extensive experiments conducted on the synthetic and real-world datasets demonstrate that, TMTC can resist data tampering and good-bad mouth attacks effectively. And whether in a dense or uniform scene, it outperforms two benchmark methods by increasing malicious node detection rate by 13.37%-21.87% and reducing cost by 18.8%-50.32%.

Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms

Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms

Research on trust mechanism of supply chain finance under Industrial Internet embedded with blockchain.

In traditional supply chain finance, the financing of enterprise mainly relies on the credit segmentation of the core enterprise, resulting in a short trust transmission radius and poor financing ability. The development of Internet technology, while expanding financing channels, has also seen an increasing severity in issues such as information fraud and data breaches, which has further aggravated the trust crisis in supply chain finance. This paper integrates blockchain technology into the industrial internet platform and analyzes the applicability of both in empowering supply chain financial trust. Then a supply chain financial trust framework, which emphasizes information sharing, data security, and trust circulation, is proposed. Furthermore, combined with the theories of Funk-SVD and entropy value, this paper designs a global trust evaluation mechanism that facilitates the trust circulation in supply chain finance and proposes a recommendation algorithm for global trust. With the testing conducted using the Epinions dataset, it is found that the algorithm proposed in this paper has a strong data dimensionality reduction and concentration ability, especially for large sample data, it can obtain more accurate evaluation values with less space occupation, thus enhancing the trust circulation ability of supply chain finance. Finally, the paper puts forward specific policy recommendations for the implementation of the supply chain finance information mechanism, aiming to better improve the financing accessibility of enterprises in supply chain, particularly small and medium-sized enterprises.

DTPBFT:A dynamic and highly trusted blockchain consensus algorithm for UAV swarm

With the continuous development of UAV technology, the application of UAV swarm in the military field is gradually becoming the focus of research around the world. Although it can bring a series of benefits in autonomous cooperation, the traditional UAV management technology is prone to hacker attacks due to many security issues such as a single point of failure brought by centralized management. Because of the advantages of distributed, tamper-proof, and traceability, blockchain is applied to UAV swarm to solve some of the security problems caused by centralized management. However, due to the limitations of its consensus algorithm Practical Byzantine Fault Tolerance (PBFT), its communication complexity will increase rapidly with the increase of the number of nodes, which also leads to the poor scalability of the algorithm and can only be applied to small-scale networks. To use the PBFT algorithm in large-scale networks such as UAV swarm, a dynamic and highly trusted PBFT (DTPBFT) algorithm is proposed in this paper. Firstly, a new consensus algorithm model including consensus layer and verification layer is designed. Then, a consensus node election scheme based on trust mechanism is proposed under this model. The trust degree of nodes in the blockchain network is comprehensively evaluated by selecting several representative indicators, and the weight factor of each indicator is calculated by entropy weight method. It not only reduces the communication complexity, but also ensures the reliability and dynamic update of consensus nodes. Experiments show that when the number of UAVs is 200, the consensus time of DTPBFT is 0.24 s, which indicates that this algorithm can support large-scale UAV swarm without causing communication congestion, so it has good scalability. In addition, experiments also show that DTPBFT can tolerate more than 13 malicious nodes, which improves the fault tolerance rate of PBFT.

Analysis of trust factors for AI-assisted diagnosis in intelligent Healthcare: Personalized management strategies in chronic disease management

By utilizing various techniques to integrate the Unified Theory of Acceptance and Use of Technology (UTAUT) model with the variables of personal innovation and task complexity, an initial trust mechanism is established. Based on the inherent motivation and demand of doctors to use AI-assisted diagnosis systems, a doctor adoption model for AI-assisted diagnosis systems is established. Using chronic disease management as an example, personalized management strategies based on reinforcement learning technology are discussed for a virtual management environment that provides intervention recommendations according to the medical condition. It is shown in experiments that the fitting goodness-of-fit value of the model for willingness to use, which excludes initial trust-related factors, is only 0.557, while the fitting goodness-of-fit value of the proposed model in this paper is 0.673. This indicates that the model that includes initial trust factors can more accurately explain the logic of the behavior evolution of doctors adopting AI-assisted diagnosis systems.

Explainable Federated Medical Image Analysis Through Causal Learning and Blockchain.

Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhead and data heterogeneity. This paper proposes novel FL techniques using explainable artificial intelligence (XAI) for efficient, accurate, and trustworthy analysis. A heterogeneity-aware causal learning approach selectively sparsifies model weights based on their causal contributions, significantly reducing communication requirements while retaining performance and improving interpretability. Furthermore, blockchain provides decentralized quality assessment of client datasets. The assessment scores adjust aggregation weights so higher-quality data has more influence during training, improving model generalization. Comprehensive experiments show our XAI-integrated FL framework enhances efficiency, accuracy and interpretability. The causal learning method decreases communication overhead while maintaining segmentation accuracy. The blockchain-based data valuation mitigates issues from low-quality local datasets. Our framework provides essential model explanations and trust mechanisms, making FL viable for clinical adoption in medical image analysis.

A trusted mechanism against device reputation attacks in Web‐of‐Things applications

A trusted mechanism against device reputation attacks in Web‐of‐Things applications

Social media for diffusion of conflict & violence in Ethiopia: Beyond gratifications

This study explored the use of social media among the Ethiopian youth at the time of the political reform. The study employed an explanatory sequential design. Using an online questionnaire, the quantitative data were gathered from 412 university students who were pursuing their education in eight major cities of the country. For the qualitative data, in-depth interviews were conducted with 35 students in four places of the research area. The survey data was discussed using descriptive statistics while data from the in-depth interviews were discussed thematically. This research discerns that the youth had unshakeable loyalty to Telegram (96 %) while they are also dominantly using YouTube (nearly 79 %), Facebook (65 %) and Instagram (53.7 %). It is also discovered that the youths showed a high level of trust on social media (70 %) virtually on par with their faith in their friends (77.4 %) and teachers (72 %) where their entire trust in social media is governed by selective trust mechanism. The entire dependence the youths had on this mechanism, which is exacerbated by the social media algorithm, led to high level of like and share of posts including unproved stories and misinformation which in turn lead to the diffusion of conflict and violence in a vicious cycle. By shedding new light on how members of a collective culture could use the media to achieve group-oriented goals, the study also refutes the widespread view, which predominantly ties UGT (Use and Gratification Theory) with self-oriented aspirations. This study recommends the enhancement of media literacy by incorporating it in the schools curricula and providing media literacy education to the public using various channels of communication (including both mainstream and new media).

Small talk and knowledge sharing: a moderated dual-facilitating pathway model based on interpersonal trust and perceived similarity

PurposeThis study aims to investigate how small talk interaction affects knowledge sharing, examining the mediating role of interpersonal trust (affect- and cognition-based trust) and the moderating role of perceived similarity among the mechanisms of small talk and knowledge sharing.Design/methodology/approachThis research conducts complementary studies and collects multi-culture and multi-wave data to test research hypotheses and adopts structural equation modeling to validate the whole conceptual model.FindingsThe research findings first reveal two trust mechanisms linking small talk and knowledge sharing. Meanwhile, the perceived similarity between employees, specifically, strengthens the affective pathway of trust rather than the cognitive pathway of trust.Originality/valueThis study combines Interaction Ritual Theory and constructs a dual-facilitating pathway approach that aims to reveal the impact of small talk on knowledge sharing, describing how and when small talk could generate a positive effect on knowledge sharing. This research provides intriguing and dynamic insights into understanding knowledge sharing processes.