Trust Evaluation Research Articles

A Data Transmission Scheme Based on Reinforcement-Learning-Aided Two-Stage Trust Evaluation for UASNs

A Data Transmission Scheme Based on Reinforcement-Learning-Aided Two-Stage Trust Evaluation for UASNs

Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

ABSTRACTIn the context of Mobile Ad Hoc Networks (MANETs), the dynamic and decentralized topology poses significant challenges like unreliable connectivity, limited bandwidth, node mobility, and vulnerability to security threats from malicious nodes. Ensuring secure and energy‐efficient data transmission in such environments is crucial for mission‐critical applications. This research addresses these pressing challenges by introducing a robust routing protocol capable of detecting and mitigating malicious nodes, thereby enhancing MANET's Quality of Service (QoS). The proposed approach, the Dendritic Cell with Adaptive Trust Q‐learning Protocol (dDC‐ATQP), integrates several innovative techniques to tackle these issues. Firstly, the trust evaluation mechanism assesses the behavior of nodes to identify potential malicious actors, mitigating the effect of malicious nodes on system execution. Secondly, the adaptive routing strategy optimizes data transmission paths based on real‐time network conditions, reducing latency and packet loss. To evaluate the effectiveness of this approach, extensive simulations are conducted using a range of performance metrics. The results demonstrate significant improvements over existing methods, including a throughput increase of (79.2% in 50 s), lower end‐to‐end‐delay (0.075 s for 20 nodes), energy consumption of (38.55/J), higher packet delivery ratio (98% for 20 nodes), reduced packet loss ratio (5% for 100 nodes), enhanced security (80% for 70 nodes).

Research on Network Security Protection Technology Based on P2AEDR in New Low-Voltage Control Scenarios for Power IoT and Other Blockchain-Based IoT Architectures

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud–edge interaction and cloud–cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures.

TrustGNN: Graph Neural Network-Based Trust Evaluation via Learnable Propagative and Composable Nature.

Trust evaluation is critical for many applications such as cyber security, social communication, and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN-based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.

Evaluation of Patients' Trust in Dentists According to Generations

Evaluation of Patients' Trust in Dentists According to Generations

Comprehensive Analysis Of Background Screening Impact On Employee Experience And Organizational Outcomes

This comprehensive study examines the multifaceted impact of background checks on employee experience and organizational outcomes, with a particular focus on the implementation of a nationwide, standardized employment screening system. The research is guided by six interconnected themes: trust, transparency, fairness, diversity and inclusion, engagement, and retention. In the contemporary business landscape, background checks have transcended their traditional role as a mere formality, evolving into critical assets for enhancing employee satisfaction, organizational trust, and retention rates. This review synthesizes a wide array of studies to analyze the complex interplay between background screening practices and employer-employee relationships. It establishes robust evidence for how fair, transparent, and reasonable background checks can significantly enhance organizational commitment and employee loyalty. The results underscore the critical importance of awareness and transparency in the background check process, highlighting how these factors foster trust and perceptions of fairness, ultimately leading to positive employee attitudes and improved organizational outcomes. The study also delves into the potential adverse effects of excessive or biased background checks on workforce diversity, with a particular emphasis on historically marginalized groups and individuals with criminal records. It proposes that employers who embrace equality in the workplace and ensure that their background check processes are free from unlawful discrimination can cultivate and maintain a diverse, inclusive, and highperforming workforce. The implications for practice and research are far-reaching. To optimize employee experience and key outcomes such as organizational commitment, job satisfaction, and reduced turnover intention, employers must prioritize fairness, transparency, and inclusiveness in their background check processes. The study identifies critical areas for further research, including sector-specific effects of background screening practices and the impact of emerging technologies and data privacy concerns on background check processes. By reconceptualizing background checks as a strategic tool for building trust and enhancing employee commitment, rather than a mere compliance exercise, organizations can achieve superior outcomes across multiple dimensions of performance. This research provides a comprehensive framework for understanding and implementing effective background screening practices that balance organizational risk management with positive employee experiences.

Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach

Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach

Fuzzy based Energy Efficient Rider Remora Routing protocol for Secured Communication in WSN Network

Due to the resource constraint nature of WSN, ensuring secured communication in WSN is a challenging problem. Moreover, enhancing the network lifetime is one of the major issues faced by the existing studies. So, in order to secure the communication between WSNs and achieve improved network lifetime, a novel trust enabled routing protocol is proposed in this study. Initially, the clusters are constructed using Direct, Indirect, and Total Trust evaluations, which helps to identify the faulty nodes. After, an Improved Fuzzy-based Balanced Cost Cluster Head Selection (IFBECS) method is used to choose the cluster head (CH). Finally, to determine the best path from source to destination, a hybrid bionic energy-efficient routing model known as an Energy Efficient Rider Remora Routing (EERRR) protocol is introduced. To improve the network lifetime and throughput, the parameters like remaining energy of the CH, sensor node space, CH, etc., are considered by the utilized protocol. The proposed mechanism is implemented in NS-2 programming tool. The simulation results show that the proposed routing protocol has attained improved PDR of 97.92% at the time period of 50ms, reduced energy consumption of 3.336 at the time period of 100ms, higher throughput of 86262.7 at the time period of 250ms, and enhanced network lifetime of 1028.08 rounds in 200 nodes. Therefore, by attaining better results as compared with other existing protocols, it is clearly revealed that the proposed routing protocol is highly suitable for secured energy efficient WSN communication.

Trust-aware and improved density peaks clustering algorithm for fast and secure models in wireless sensor networks

Many trust-based models for wireless sensor networks do not account for trust attacks, which are destructive phenomena that undermine the security and reliability of these models. Therefore, a trust-based fast security model fused with an improved density peaks clustering algorithm (TFSM-DPC) is proposed to quickly identify trust attacks in this paper. First, when calculating direct trust values, TFSM-DPC designs the adaptive penalty factors based on the state of received and sent packets and behaviors, and introduces the volatilization factors to reduce the effect of historical trust values. Second, TFSM-DPC improved density peaks clustering (DPC) algorithm to evaluate the trustworthiness of each recommendation value, thus filtering malicious recommendations before calculating the indirect trust values. Moreover, to filter two types of recommendations, the improved DPC algorithm incorporates artificial benchmark data along with trust values recommended by neighbors as input data. Finally, based on the relationship between direct trust and indirect trust, a secure formula for calculate the comprehensive trust is designed. Therefore, the proposed TFSM-DPC can improve the accuracy of trust evaluation and speed up the identification of malicious nodes. Simulation results show that TFSM-DPC can effectively identify on-off, bad-mouth and collusion attacks, and improve the speed of excluding malicious nodes from the network, compared to other trust-based algorithms.

Medical Metaphors That May Reinforce Misconceptions Are Associated With Increased Trust in the Clinician.

This study measured patient reactions to medical metaphors used in musculoskeletal specialty offices and asked: (1) Are there any factors associated with patient thoughts and emotions in response to common metaphors? (2) Is there a difference between patient ratings of metaphors rated as potentially reinforcing misconceptions and those that are more neutral? In a cross-sectional study, 228 patients presenting to multiple musculoskeletal specialty offices rated reactions to 4 metaphors presented randomly from a set of 14. Two were categorized as potentially reinforcing common misconceptions and 2 as relatively neutral. Bivariate tests and multivariable regression identified factors associated with patient ratings of levels of emotion (using the standard assessment manikins) and aspects of experience (communication effectiveness, trust, and feeling comfortable rated on 11-point ordinal scales) in response to each metaphor. Levels of patient unhelpful thinking or distress regarding symptoms were not associated with patient ratings of patient emotion and experience in response to metaphors. Metaphors that reinforce misconceptions were associated with higher ratings of communication effectiveness, trust, and comfort (P < .05). The observation that metaphors that validate a person's understanding of his or her illness may elicit trust even if those metaphors have the potential to reinforce misconceptions may account for the common usage of such metaphors. Clinicians can work to incorporate methods for building trust without reinforcing misconceptions.

A Dynamic Trust evaluation and update model using advance decision tree for underwater Wireless Sensor Networks

Underwater wireless sensor networks (UWSNs) are an emerging research area that is rapidly gaining popularity. However, it has several challenges, including security, node mobility, limited bandwidth, and high error rates. Traditional trust models fail to adapt to the dynamic underwater environment. Thus, to address these issues, we propose a dynamic trust evaluation and update model using a modified decision tree algorithm. Unlike baseline methods, which often rely on static and generalized trust evaluation approaches, our model introduces several innovations tailored specifically for UWSNs. These include energy-aware decision-making, real-time adaptation to environmental changes, and the integration of multiple underwater-specific factors such as water currents and acoustic signal properties. Our model enhances trust accuracy, reduces energy consumption, and lowers data overhead, achieving a 96% accuracy rate with a 2% false positive rate. Additionally, it outperforms baseline models by improving energy efficiency by 50 mW and reducing response time to 20 ms per packet. These innovations demonstrate the proposed model’s effectiveness in addressing the unique challenges of UWSNs, ensuring both security and operational efficiency goals. The proposed model effectively enhances the trust evaluation process in UWSNs, providing both security and operational benefits. These key findings validate the potential of integrating modified decision tree algorithms to improve the performance and sustainability of UWSNs.

Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things

The Internet of Underwater Things (IoUT) represents an emerging and innovative field with the potential to revolutionize underwater exploration and monitoring. Despite its promise, IoUT faces significant challenges related to reliability and security, which hinder its development and deployment. A particularly critical issue is the establishment of trustworthy communication networks, necessitating the adaptation and enhancement of existing models from terrestrial and marine systems to address the specific requirements of IoUT. This work explores the problem of dishonest recommendations within trust modelling systems, a critical issue that undermines the integrity of trust modelling in IoUT networks. The unique environmental and operational constraints of IoUT exacerbate the severity of this issue, making current detection methods insufficient. To address this issue, a recommendation evaluation method that leverages both filtering and weighting strategies is proposed to enhance the detection of dishonest recommendations. The model introduces a filtering technique that combines outlier detection with deviation analysis to make initial decisions based on both majority outcomes and personal experiences. Additionally, a belief function is developed to weight received recommendations based on multiple criteria, including freshness, similarity, trustworthiness, and the decay of trust over time. This multifaceted weighting strategy ensures that recommendations are evaluated from different perspectives to capture deceptive acts that exploit the complex nature of IoUT to the advantage of dishonest recommenders. To validate the proposed model, extensive comparative analyses with existing trust evaluation methods are conducted. Through a series of simulations, the efficacy of the model in capturing dishonest recommendation attacks and improving the accuracy rate of detecting more sophisticated attack scenarios is demonstrated. These results highlight the potential of the model to significantly enhance the trustworthiness of IoUT establishments.

Multi-Dimensional Consensus Node Trust Evaluation and Selection for Blockchain-Empowered Electric Measurement Data Traceability

Multi-Dimensional Consensus Node Trust Evaluation and Selection for Blockchain-Empowered Electric Measurement Data Traceability

Enhancing fairness, transparency and accountability during tendering under Ghana’s procurement system: a systematic review

PurposeGhana’s legal framework for procurement has undergone substantial reform to increase its efficacy. However, disregard for legal obligations set forth has resulted in issues of fraud, corruption and poor oversight. This study seeks to synthesize literature on the recognition of legal obligations arising from tendering procedures and measures to promote fairness, transparency and accountability under Ghana’s procurement framework.Design/methodology/approachLegal frameworks and publications from diverse countries have been synthesized using a systematic literature review across three databases (Scopus, JSTOR and HeinOnline) to illuminate key concepts, issues and best practices relevant to the study. Data obtained from included publications was synthesized using Sandelowski and Barroso’s two-step approach by using a qualitative meta-summary and thematic synthesis.FindingsThe study reveals that issues of conflict of interest, corruption, lack of capacity, inadequate oversight and insufficient legal follow-through hinder the effectiveness of procurement regulations. The findings highlight the need for targeted improvements in resource allocation for consistent application of transparency measures, regular publication of notices and robust enforcement of accountability mechanisms. The report proposes the creation of a common data environment for networking and information dissemination, implementing feedback systems and trust rating schemes.Practical implicationsThe study contributes to the body of knowledge on procurement regulation by providing a thorough analysis of Ghana’s procurement framework. The findings will help policymakers close the observed implementation gaps by guiding the revision of current legislation and the introduction of new regulations. Research findings can be used to guide the creation of focused training courses.Originality/valueThis study, one of the first of its kind in Ghana, examines the current procurement framework, including legal obligations and implementation challenges. It contributes to the body of knowledge on the subject by providing a current and fact-based analysis as well as relevant recommendations for strengthening the framework.

Can Optimized Genetic Algorithms Improve the Effectiveness of Homestay Recommendation Systems in Smart Villages? A Case of Thailand

ABSTRACTThis paper introduces a novel approach to optimize genetic algorithms (GAs) for homestay recommendation systems, specifically designed for smart village tourism destinations. Researchers developed an advanced GA focused on maximizing user satisfaction, the main quality metric. The algorithm was tailored to address the dynamic nature of homestay offerings and the varied preferences of travelers, using users' reviews, listing attributes, and historical booking data. The GA framework included a custom encoding scheme, fitness function, and parameters. Validation occurred through a case study in a smart village, with the algorithm's effectiveness tested via user surveys and ratings. Results showed that GA‐driven recommendations surpassed traditional methods, enhancing user satisfaction, trust, and booking rates while benefiting hosts with positive reviews. The optimized GA improved recommendation accuracy and efficiency, boosting economic benefits for local communities and contributing significantly to recommendation system research.

TrustGuard: GNN-Based Robust and Explainable Trust Evaluation With Dynamicity Support

TrustGuard: GNN-Based Robust and Explainable Trust Evaluation With Dynamicity Support

User preferences and trust in hypothetical analog, digitalized and AI-based medical consultation scenarios: An online discrete choice survey

Current developments in telemedicine and artificial intelligence (AI) are significantly impacting doctor-patient interactions. This study examined the interacting role of individual traits with different levels of digitalization in participants' user preferences and trust within hypothetical medical scenarios. Specifically, preferences and trust levels towards various digitalized and analog formats, such as face-to-face interactions, video calls, written exchanges with a doctor or chatbot, or conversations with AI avatars were compared using standard scenarios of varying health risks and potentially embarrassing content.In an online discrete choice experiment, 1009 participants rated hypothetical scenarios of varying medical concerns regarding their preferred conversation format and trust. User preference (n = 2018 observations) and trust (n = 9880 observations) were predicted using two multilevel models.Higher perceived efficiency of digital conversation formats predicted user preference for digitalized formats. However, users’ preference for digitalized formats was generally lower compared to face-to-face interactions, especially when receiving bad news. The level of digitalization was negatively associated with trust, which was lower for consultations that involved receiving bad news or discussing potentially embarrassing content compared to good news. Trust ratings varied depending on the conversation topic.When comparing analog and digitalized medical consultation scenarios, digitalized medical consultations are not equally suited for every medical consultation. Participants preferred personal contact, particularly when bad news needed to be communicated. Additionally, trust in the doctor significantly varies depending on the topic of conversation.

BSPM: Blockchain-based Security Protection Model for power load management terminals

Power load management terminals are essential components of the smart grid system. However, they encounter numerous security challenges owing to their widespread deployment across large areas. These nodes exist in uncontrollable physical environments and are vulnerable to malicious attacks. Researching security measures for these terminals is vital to enhancing the security of the smart grid. Blockchain technology, with its distributed storage and tamper-proof features, can effectively protect the data security of these terminals. This paper proposes a blockchain-based security protection model for power load management terminals. First, the architecture of the model is designed using blockchain technology. In addition, a dynamic trust evaluation method for terminals is established through the beta reputation system. The blockchain consensus algorithm for power load management terminals is designed based on the trust evaluation value and practical Byzantine fault tolerance. The effectiveness of the trust evaluation model is verified by simulating the terminal operating situation. The message complexity and storage requirements of the security protection model are analyzed by comparing them with results from related studies.

Trust value evaluation of cloud service providers using fuzzy inference based analytical process

Users can purchase virtualized computer resources using the cloud computing concept, which is a novel and innovative way of computing. It offers numerous advantages for IT and healthcare industries over traditional methods. However, a lack of trust between CSUs and CSPs is hindering the widespread adoption of cloud computing across industries. Since cloud computing offers a wide range of trust models and strategies, it is essential to analyze the service using a detailed methodology in order to choose the appropriate cloud service for various user types. Finding a wide variety of comprehensive elements that are both required and sufficient for evaluating any cloud service is vital in order to achieve that. As a result, this study suggests an accurate, fuzzy logic-based trust evaluation model for evaluating the trustworthiness of a cloud service provider. Here, we examine how fuzzy logic raises the efficiency of trust evaluation. Trust is assessed using Quality of Service (QoS) characteristics like security, privacy, dynamicity, data integrity, and performance. The outcomes of a MATLAB simulation demonstrate the viability of the suggested strategy in a cloud setting.

Priv-Share: A privacy-preserving framework for differential and trustless delegation of cyber threat intelligence using blockchain

The emergence of the Internet of Things (IoT), Industry 5.0 applications and associated services have caused a powerful transition in the cyber threat landscape. As a result, organisations require new ways to proactively manage the risks associated with their infrastructure. In response, a significant amount of research has focused on developing efficient Cyber Threat Intelligence (CTI) sharing. However, in many cases, CTI contains sensitive information that has the potential to leak valuable information or cause reputational damage to the sharing organisation. While a number of existing CTI sharing approaches have utilised blockchain to facilitate privacy, it can be highlighted that a comprehensive approach that enables dynamic trust-based decision-making, facilitates decentralised trust evaluation and provides CTI producers with highly granular sharing of CTI is lacking. Subsequently, in this paper, we propose a blockchain-based CTI sharing framework, called Priv-Share, as a promising solution towards this challenge. In particular, we highlight that the integration of differential sharing, trustless delegation, democratic group managers and incentives as part of Priv-Share ensures that it can satisfy these criteria. The results of an analytical evaluation of the proposed framework using both queuing and game theory demonstrate its ability to provide scalable CTI sharing in a trustless manner. Moreover, a quantitative evaluation of an Ethereum proof-of-concept prototype demonstrates that applying the proposed framework within real-world contexts is feasible.