Trust Mechanism Research Articles

Securing decentralized federated learning: cryptographic mechanisms for privacy and trust

Securing decentralized federated learning: cryptographic mechanisms for privacy and trust

A novel collaborative privacy protection scheme based on verifiable secret sharing and trust mechanism

A novel collaborative privacy protection scheme based on verifiable secret sharing and trust mechanism

Trustworthy AI: Securing Sensitive Data in Large Language Models

Large language models (LLMs) have transformed Natural Language Processing (NLP) by enabling robust text generation and understanding. However, their deployment in sensitive domains like healthcare, finance, and legal services raises critical concerns about privacy and data security. This paper proposes a comprehensive framework for embedding trust mechanisms into LLMs to dynamically control the disclosure of sensitive information. The framework integrates three core components: User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control. By leveraging techniques such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Named Entity Recognition (NER), contextual analysis, and privacy-preserving methods like differential privacy, the system ensures that sensitive information is disclosed appropriately based on the user’s trust level. By focusing on balancing data utility and privacy, the proposed solution offers a novel approach to securely deploying LLMs in high-risk environments. Future work will focus on testing this framework across various domains to evaluate its effectiveness in managing sensitive data while maintaining system efficiency.

The Effect of Social Media Marketing on Tourist Loyalty: a Mediation - Moderation Analysis in the Tourism Sector under the Stimulus-Organism-Response Model

This study investigates the impact of social media marketing (SMM) activities on destination loyalty, emphasizing the mediating roles of tourist satisfaction and engagement, and the moderating role of destination trust in an extended Stimulus-Organism-Response (S-O-R) model in the context of an emerging country (Vietnam). Data from 514 domestic tourists collected in Vietnam's coastal destinations were analyzed using partial least squares structural equation modeling (PLS-SEM). The results reveal significant direct effects of SMM on tourist satisfaction, engagement, and loyalty. Satisfaction and engagement mediate the relationship between SMM and loyalty, while destination trust strengthens these effects. This study offers significant theoretical contributions to the field of tourism marketing by extending the application of the S-O-R framework. It demonstrates how SMM influences destination loyalty not only through direct pathways but also through indirect mechanisms involving tourist satisfaction and tourist engagement. By examining these mediating and moderating roles, the study expands our understanding of how emotional and behavioral responses to SMM drive destination loyalty. The findings suggest that destination marketing organizations (DMOs) should develop SMM strategies that not only enhance engagement and satisfaction but also incorporate trust mechanisms to foster long-term loyalty, particularly in emerging tourism markets.

Research of Federated Learning Application Methods and Social Responsibility

Federated learning is a multi-party distributed machine learning system that allows each participant to complete the training task without their data out of the locality. The real meaning of the realization of data availability is invisible to protect personal privacy and data security. Although some institutions need to obtain their data's benefit by sharing their own data sets, the potential trust risks lead to the data not being better utilized cooperatively. In addition,regulatory requirements on data computing and sharing are different in various countries. These contribute to enormous challenges in the actual application of federated learning. In this research, we make a comprehensive review of the application patterns of federated learning in different fields. Then, we illustrate the social responsibilities of federated learning from four dimensions, including compliance application, system security mechanism, the trust mechanism, and ethical security. Finally, based on the current characteristics and regulatory requirements of federated learning, we discuss the future research directions for federated learning.

Trust driven On-Demand scheme for client deployment in Federated Learning

Containerization technology plays a crucial role in Federated Learning (FL) setups, expanding the pool of potential clients and ensuring the availability of specific subsets for each learning iteration. However, doubts arise about the trustworthiness of devices deployed as clients in FL scenarios, especially when container deployment processes are involved. Addressing these challenges is important, particularly in managing potentially malicious clients capable of disrupting the learning process or compromising the entire model. In our research, we are motivated to integrate a trust element into the client selection and model deployment processes within our system architecture. This is a feature lacking in the initial client selection and deployment mechanism of the On-Demand architecture. We introduce a trust mechanism, named “Trusted-On-Demand-FL”, which establishes a relationship of trust between the server and the pool of eligible clients. Utilizing Docker in our deployment strategy enables us to monitor and validate participant actions effectively, ensuring strict adherence to agreed-upon protocols while strengthening defenses against unauthorized data access or tampering. Our simulations rely on continuous user behavior datasets, deploying an optimization model powered by a genetic algorithm to efficiently select clients for participation. By assigning trust values to individual clients and dynamically adjusting these values, combined with penalizing malicious clients through decreased trust scores, our proposed framework identifies and isolates harmful clients. This approach not only reduces disruptions to regular rounds but also minimizes instances of round dismissal, Consequently enhancing both system stability and security.

Actors of the Political Socialization of the Youth

The formation of political identity of contemporary Russian youth is a dynamic socialization process involving constant communication with various actors and reassessment of norms and beliefs in political and cultural media discourse. Using the methods of actor-network theory, we can better understand how individual groups, accounts construct political identity through cultural narratives and practices by means of their affordances. Such properties of actors build trust in them in the case of discursive coincidences and common frames of self-identity within the network. By examining the practices of actors and the effects of their affordances in political socialization, we can understand how these processes shape the collective beliefs and the practices of young people that follow.The purpose of the article is to identify the sources of political socialization of student youth as actors by examining the trust mechanisms that contribute to successive processes of socialization formation.Methods of survey, focus groups, text analysis, actor-network theory methods, and Python, Gephi, and Google Excel programs were applied to map and represent the results.The study analyzed essays, surveys, and interviews (300 students in total) in order to qualitatively identify actors that influence the political socialization of young people. These actors include official cultural policies, family, school, media and social networks, church, society, political leaders and bloggers. The research allowed us to identify the most stable cognitive, affective and practical components of these actors’ discourses, emphasizing the special significance of cultural and historical facts in the formation of political identity.The process of political socialization begins in childhood and continues in the following stages of personal development, always intertwined with one or another social reflection, forming a coherent picture of the world. Young people trust information supported by facts and consistent with their worldview; young people are influenced by parents, official sources and bloggers. In the political sphere, young people are interested in political leaders, elections, the work of government bodies, and the influence of politics on areas of life such as culture and ecology.The analysis conducted in the optics of ANT has revealed the mechanisms of how student youth are influenced by various actors and how this influence shapes their political beliefs and behavior. The study emphasizes the importance of constant communication for the formation of youth political identity through the influence of various actors in media discourse.

Exploration of the application of blockchain in e-government: Opportunities and risks coexist

Blockchain technology provides a technical solution for the challenges faced by e-government, such as low efficiency, excessive energy consumption, and lack of trust mechanisms. It can promote the establishment of a more efficient and high-quality government service system, thereby enhancing the public trust of the government. Although blockchain technology provides theoretical innovation to traditional governance structures with its decentralization, consensus mechanisms, and enhanced trust, its impact and ability to transform existing governance structures still have limitations in practical applications. Based on an in-depth analysis of the concept and application boundaries of blockchain, this article explores its value in the field of e-government. At the same time, it systematically proposes strategic suggestions to promote the development of blockchain technology in the field of e-government from multiple aspects such as institutional construction, technical standardization, and professional talent cultivation, in response to its limitations and application risks. By accelerating technology research and development and standard construction, strengthening high-level promotion, optimizing promotion competition mechanisms, and conducting rooted research, the application and promotion of blockchain in government governance can be accelerated, laying a solid foundation for building a future intelligent and transparent government service model.

Innovative Service Design of Online Agricultural Product Platforms: Improving User Experience and Trust

The rapid growth of online agricultural product platforms has revolutionized traditional agricultural markets, offering consumers more convenient purchasing options and expanding sales channels for farmers. However, challenges such as the lack of product transparency, inadequate trust mechanisms, and varied user needs hinder the platforms' development. This study explores innovative service design strategies to enhance user experience and build trust in online agricultural product platforms, guided by the Double Diamond Model. By identifying critical obstacles and addressing them through digitalization, AI-driven analytics, and ethical farming practices, this research proposes a user-centered service innovation framework. The study emphasizes intelligent interface design, product traceability mechanisms, and interactive consumer feedback systems to foster trust and engagement. Through iterative prototyping, testing, and optimization, this approach aims to create a sustainable and reliable platform ecosystem that meets diverse user needs. The findings provide actionable insights for online agricultural product platforms to improve competitiveness, user satisfaction, and long-term market sustainability.

Research on the Collaborative Mechanism of Value Creation in Public–Private Partnership Projects Under Dynamic Trust

ABSTRACTPublic–private partnership (PPP) is now focused on project value creation, with trust playing a crucial role. This study models trust evolution among stakeholders using evolutionary game theory and simulations, revealing that higher initial and sustained trust boosts project value. However, overreliance on trust can lead to deregulation and neglect of contracts, harming outcomes. Spillover effects and speculative behavior also threaten value creation. The study identifies an optimal incentive range for reputation gains, highlighting the need for balanced trust mechanisms, effective incentives, and penalties to ensure collaboration and project success.

Research on the Change Mechanism of Expert Credibility of Chinese Internet Platforms from the Perspective of Cognitive Balance

Abstract: The spread of new media technologies in China has facilitated knowledge dissemination and public opinion exchange, but the COVID-19 pandemic has compounded the complexity and challenges of this process. During the pandemic, experts made frequent statements through new media, making it difficult to distinguish the truth from the truth, resulting in information overload and an increase in the burden of public cognition. Experts' forecast errors and other events lead to the public's questioning and resolution of the credibility of experts, and even produce boredom, affecting the authority of experts' remarks. This study aims to explore the causes of this phenomenon and the trend of expert credibility. It not only refines the analysis framework of the factors affecting expert credibility but also reveals the complex mechanism of expert specialization, public cognition, and event domain, providing valuable insights for understanding and improving the trust of experts in the public. By combining the cognitive balance theory, this study uses case analysis and text analysis to conduct an in-depth study on the expert videos and their comments on TikTok. The results show that there is a positive correlation between expert credibility and public attitude consistency. In addition, there is a significant negative correlation between the credibility of experts and the degree of public cognition, but the relationship between the degree of expert specialization and the scope of the event field is weak. Finally, this study proposes that diversification and information transmission characteristics should be considered to construct expert trust mechanisms in social media environments.

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.

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.

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.