Trust Prediction Research Articles

Reliable Routing for V2X Networks: A Joint Perspective of Trust Prediction and Attack Resistance

Reliable Routing for V2X Networks: A Joint Perspective of Trust Prediction and Attack Resistance

Advanced trust classification in social networks using a triple generative adversarial network-assisted capsule network enhanced by gannet optimization

Over the past ten years, social networks (SN) have evolved into the primary infrastructure for people's everyday activities. Trust classification in social networks involves evaluating the trustworthiness of users or the information they share. Traditional trust classification methods often rely on explicit features, such as user ratings, reviews, and social ties. These methods utilize rule-based to assign trust scores for users or entities. However, they face challenges in capturing the exact nature of trust. In this manuscript, Advanced Trust Classification in Social Networks using a Triple Generative Adversarial Network-Assisted Capsule Network Enhanced by Gannet Optimization (Trust-TripleGAN-CapsNet-GOA) is proposed. In this manuscript, the feature vector has computed to every social network users pair after the raw data from the Sentiment140 dataset, is analysed. The membership of trust is then ranked according to five-class classifications by incorporating Tanimoto Trust Similarity coefficient. Then, Triple Generative Adversarial Network-Assisted Capsule Network (TripleGAN-CapsNet) is used to categorise the trust values of users. Finally, the weight parameters of TripleGAN-CapsNet is optimized by the Gannet Optimization Algorithm (GOA) to enhance the accuracy of the trust behaviour in SN. The proposed Trust-TripleGAN-CapsNet-GOA method attains 22.94 %, 32.36 % and 21.96 % higher accuracy, 30.27 %, 19.46 % and 12.39 %, higher precision when analyzed with the existing models, such as a method for trust mirroring assessment under social networks parameters with fuzzy system (Trust-SNP-FS), deep matrix factorization in social networks for trust-aware recommendation (Trust-DMF-SN) and towards time-aware context-aware deep trust prediction on the online social networks (TACADTrust-SN). The simulation outcomes exhibit that the proposed method attains 99 % accuracy.

Using attentive temporal GNN for dynamic trust assessment in the presence of malicious entities

Trust networks are built on the premise that there is a network of trust among entities. Precisely evaluating pairwise trust in such networks is essential for enhancing the quality of service in various applications, including online marketing, recommender systems, and IoT-based applications. However, it remains challenging to consider dynamic trust and account for malicious behavior in a scalable and effective manner.Recently, Graph Neural Networks (GNN)-based methods have emerged as powerful and scalable tools for trust estimation. Nevertheless, they lack thorough consideration for the time-dependent nature of trust, especially in the presence of malicious behaviors, leading to inaccurate trust predictions. To address this gap, we propose a novel trust assessment model called MATA, which handles the time dependency of trust and exhibits robustness against malicious behaviors. MATA leverages an extended Graph Convolutional Network (GCN) integrated with Recurrent Neural Networks (RNNs) and an attention mechanism to jointly capture time dependencies and trace entities’ behavior over time in complex networks. Additionally, by clustering the learned trust representations over time, we assign reputation values to entities. This allows us to monitor their dynamic behavior and mitigate potential adverse effects on trust assessment.In our experiments on real-world datasets, MATA outperforms the state-of-the-art methods by up to 8.4 % in accuracy. This improvement is a testament to the effectiveness of the proposed approach. Furthermore, our work enhances the robustness of trust assessment models, as evidenced by consistently higher performance metrics in the face of various trust-related attacks.

Blockchain solutions for trustworthy decentralization in social networks

The influence of blockchain on technology, especially in the field of finance, has brought about the age of cryptocurrencies and decentralized control, spreading its impact across various industries to ensure precision. As the trend moves towards decentralized social networks, diverse approaches are explored, becoming crucial tools for defining trust and privacy in individuals’ personal lives during uncertain environments. Blockchain’s transformative role reaches beyond finance, revolutionizing how we establish trust and privacy in our interconnected digital societies. Concurrently, researchers recognize the efficacy of trust models in enhancing the security and reliability of social networks applications. However, a comprehensive review of recent advancements in trust models based on blockchain for social networks is lacking. To address this gap, our survey takes a comprehensive perspective on the theme of trust within decentralized networks. We examine various concepts such as the trust concept itself, trust models, trust prediction methodologies, decentralization in trust-based social networks, and the role of blockchain in fortifying trust and security in social networks. We investigate the intersection of trust and blockchain technology, encompassing concepts, types of blockchain, and their applications in social networks. These models are categorized based on different parameters, and we examine both their advantages and limitations. Finally, we propose prospective paths for researchers interested in exploring this area.

CTJIF-ICN: A Coadjuvant Trust Joint Interest Forwarding Mechanism in Information Centric Networks

The Information centric networks (ICN) transforms the focal point of current Internet paradigm to data centric approach from host centric approach by allowing content driven forwarding and in-network caching mechanisms. Though NDN (Named data networking) paradigm of ICN assures a secure content communication, it is vulnerable to different attacks by the malicious nodes. To minimize the hazards from compromised nodes and to improve the network security, the remaining nodes should transparently receive information about such nodes. This will restrict the forwarding strategy to exploit these malicious nodes for forwarding interest and content as well. Our protocol introduces a dynamic model for prediction of trust in order to evaluate the node trust. Proposed approach observes the historical behaviors of node and uses extended fuzzy logic rules for the prediction of future behaviors to evaluate the node’s trust value. This prediction model is incorporated within the trust based forwarding mechanism that aims to forward interest through secure and shortest path. The extensive simulation study has been carried out to analyze the protocol performance in ns-3 driven ndnSIM-2.0 simulator for performance metrics such as data discovery latency, packet delivery ratio, network overhead, detection ratio and cache hit ratio. When we integrate our trust joint forwarding strategy to state-of-the-art protocols, their performance is significantly improved up to approximately 10-35% against stated performance measures for realistic network topology.

Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks

Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks

PERCEIVED ORGANISATIONAL SUPPORT AND ORGANISATIONAL TRUST LINK TO INNOVATIVE WORK BEHAVIOUR BY THE MEDIATION OF TACIT KNOWLEDGE SHARING

The aim of this study is to determine how perceived organisational support and organisational trust affect innovative work behaviour through the mediation mechanism of tacit knowledge sharing in a public organisation context in Nigeria. Using the cross-sectional research design, data were randomly collected from 258 employees from the Delta State Ministry of Health, which was subjected to the partial least square (PLS) analytical procedure for hypothesis testing. The PLS analysis results suggested that while tacit knowledge sharing mediates perceived organisational support and organisational trust link to innovative work behaviour, it also mediates perceived organisational support link to innovative work behaviour sequentially with organisational trust as an antecedent. The study concluded that perceived organisational support and organisational trust prediction of innovative work behaviour can be explained by tacit knowledge sharing. The study puts forward important practical implications for organisations/management to follow in effectuating the linkages among the constructs in a meaningful and coherent manner.

Joint item recommendation and trust prediction with graph neural networks

Item recommendation and trust prediction are desired by users on social network platforms since they can help users find their favourite items or friends faster. Existing methods usually utilize users’ social relationships to facilitate the item recommendation task and leverage users’ preferences to assist the trust prediction task. While sociological researchers have shown that users’ preferences and users’ social relationships are not isolated but influenced by each other, there are a few studies that model these two tasks jointly. However, in these studies, the incorporation of distrust relations and the dynamic item-specific mutual influence between users’ preferences and users’ social relationships are ignored. In this paper, we propose a joint learning method using graph neural networks for item recommendation and trust prediction tasks, named JoRTGNN. First, users’ distrust relations are incorporated along with trust relations to extract more potential user social relationships, which can benefit both the item recommendation and trust prediction tasks. In addition, a dynamic item-specific mutual influence between users’ preferences and users’ social relationships is highlighted with attention mechanisms for the joint learning tasks. Experiments have been conducted on the Epinions datasets, and the experimental results show a superior performance of JoRTGNN over baselines in item recommendation and trust prediction tasks, which demonstrates that item recommendation and trust prediction can be effectively improved in the process of the mutual influence between users’ preferences and users’ social relationships.

Real-Time Trust Prediction in Conditionally Automated Driving Using Physiological Measures

Real-Time Trust Prediction in Conditionally Automated Driving Using Physiological Measures

Metapath-guided multi-headed attention networks for trust prediction in heterogeneous social networks

Trust prediction facilitates the day-to-day functionality of diverse web-based applications, such as recommendation systems, market advertising and anomaly detection. However, existing works heavily rely on user–user trust interactions, which result in limited performance as the data sparsity. Previous studies have shown that the trust relationship between users is significantly affected by the category of items that the users interacted. In this paper, we propose a MetaTrust model, which generates redundant user-item interactions as the supplement of user–user trust to alleviate the data sparsity on trust prediction. Specifically, we propose category-aware metapaths, which generate abundant user–item–user interactions based on the common item category that users have interacted with. Further, Long Short Term Memory (LSTM) networks are utilized to mine features of multiple category-aware metapaths and their correlations. In order to filter the user–item–user interactions that are not related to the current task, the real trust relationship between users are embedd in the network with MLP. Finally, a multi-headed attention network is utilized to distinguish which metapath determines trust prediction between the current pair of users. Extensive experiments on three real-world dataset show that our proposed model can effectively achieve significant improvements over other competitive approaches and show the potential interpretability of trust building.

The Influence of Community Relation and Performance on Police Trust: A Prediction Model

This study aimed to determine the police trust in relation to police community relation and performance in District 1 of Davao del Norte, 384 residents from the district 1 of Davao del Norte participated the survey. The study used descriptive-predictive design with the utilization of mean, standard deviation, pearson-r and multiple linear regression. The findings revealed that police-community relation positively and significantly correlated with police trust (r=0.500, p<0.05), similarly, police performance positively and significantly correlated with police trust (r=0.862, p<0.05). However, the significant influence of police community relation and police performance to predict public trust, overall results indicate that indicators of police performance, such as attentiveness, reliability, fairness, manners, and responsiveness, have a stronger predictive relationship with police trust compared to indicators of police-community relations. The public relation program also shows a positive relationship with trust in both models. However, the civic action program, public information program, mass communication program, and competence do not significantly contribute to the prediction of police trust.

Modelling taxpayers’ behaviour based on prediction of trust using sentiment analysis

Fiscal systems depend on taxpayer's behaviour in terms of their willingness to comply or engage in fraud, deeply rooted in trustworthiness. To gain insights into taxpayers' perceptions and their influence on trust within taxation system, we use survey data to analyse word frequencies, sentiments, attitudes. Our approach utilizes natural language processing in conjunction with machine learning techniques. We highlight a notable correlation: taxpayers who lack trust in fiscal system tend to employ a higher frequency of negative words and exhibit limited word diversity in their expressions. The presence of negative sentiments may potentially foster fraudulent behaviours in the future.

Accuracy of BEMT and CFD on the power and trust prediction of tidal turbines

We compute the loads on a model-scale tidal turbine with Blade Element Momentum (BEM) theory and Computational Fluid Dynamics (CFD) simulations, and we compare the results with towing tank tests. CFD simulations are wall-resolved, steady, Reynolds-averaged Navier-Stokes simulations with a k − ω SST turbulence model, where only a 120◦ wedge domain with a single blade is resolved in a non-inertial frame of reference. We undertake a detailed uncertainty analysis to identify the sources of error. BEM uncertainty is computed with a Monte-Carlo approach based on the differences in the predictions of CFD and Xfoil for the sectional lift and drag coefficients,while CFD uncertainty is based on the errors due to the finite number of iterations and spatial resolution. The maximum error of CFD (8.0%) with respect to the experimental data is about half of that of BEM (15.5%) for the power (CP) and the thrust (CT) coefficients and both errors are within 4.1% for CFD and within 7.2% for BEM around the optimal tip-speed ratio (λ = 6.03). The BEM error is within the uncertainty associated with the imprecise knowledge of the sectional lift and drag coefficients. The sectional forces from CFD and BEM disagree at both the tip and the root, resulting in a substantial BEM underprediction of CP at high λ values (up to 15.5%), yet CT is well predicted (within 2.3%) at every λ. The CFD uncertainty is markedly smaller than the error, which is thus mostly due to a modelling error such as the turbulence model, the neglected effect of the support structure, the free surface, and the imprecise knowledge of the input conditions. Overall these results suggest that CFD provides both a maximum error and uncertainty that are substantially smaller than that of BEM, but both methods suffer from modelling errors that require further investigation.

A Novel Multiple Role Evaluation Fusion-Based Trust Management Framework in Blockchain-Enabled 6G Network.

Six-generation (6G) networks will contain a higher density of users, base stations, and communication equipment, which poses a significant challenge to secure communications and collaborations due to the complex network and environment as well as the number of resource-constraint devices used. Trust evaluation is the basis for secure communications and collaborations, providing an access criterion for interconnecting different nodes. Without a trust evaluation mechanism, the risk of cyberattacks on 6G networks will be greatly increased, which will eventually lead to the failure of network collaboration. For the sake of performing a comprehensive evaluation of nodes, this paper proposes a novel multiple role fusion trust evaluation framework that integrates multiple role fusion trust calculation and blockchain-based trust management. In order to take advantage of fused trust values for trust prediction, a neural network fitting method is utilized in the paper. This work further optimizes the traditional trust management framework and utilizes the optimized model for node trust prediction to better increase the security of communication systems. The results show that multiple role fusion has better stability than a single role evaluation network and better performance in anomaly detection and evaluation accuracy.

Edge Intelligence for Real-Time IoT Service Trust Prediction

Mobile Edge Computing (MEC)-based Internet of Things (IoT) systems generate trust information in a real-time and distributed manner. Predicting trustworthiness of IoT services in such an MEC environment requires new prediction strategies that cater for the aforementioned characteristics of trust information. More importantly, it is imperative to investigate how the real-time trust information could be effectively integrated into trust prediction strategies in order to capture the ever-evolving nature of trustworthiness of IoT services. In turn, such a strategy allows IoT service consumers to derive more relevant and accurate trust-based decisions. To that end, our work models trust prediction in MEC-based IoT systems as an online regularized finite-sum problem in a distributed MEC environment with a given MEC topology. We then adopt the Online Alternating Direction Method (OADM) to effectively train trust prediction models in parallel over the distributed MEC environment. OADM allows splitting the aforementioned finite-sum problem into multiple sub-problems that correspond to different local MEC environments. These sub-problems can then be solved iteratively within each local MEC environment by using the local trust data therein. This can avoid the movement of data across the core networks of mobile network providers. Experiments on real-world and synthetic datasets demonstrate the effectiveness and scalability of the proposed method.

On Manipulating Weight Predictions in Signed Weighted Networks

Adversarial social network analysis studies how graphs can be rewired or otherwise manipulated to evade social network analysis tools. While there is ample literature on manipulating simple networks, more sophisticated network types are much less understood in this respect. In this paper, we focus on the problem of evading FGA---an edge weight prediction method for signed weighted networks by Kumar et al. 2016. Among others, this method can be used for trust prediction in reputation systems. We study the theoretical underpinnings of FGA and its computational properties in terms of manipulability. Our positive finding is that, unlike many other tools, this measure is not only difficult to manipulate optimally, but also it can be difficult to manipulate in practice.

C-DeepTrust: A Context-Aware Deep Trust Prediction Model in Online Social Networks.

Trust prediction provides valuable support for decision making, information dissemination, and product promotion in online social networks. As a complex concept in the social network community, trust relationships among people can be established virtually based on: 1) their interaction behaviors, e.g., the ratings and comments that they provided; 2) the contextual information associated with their interactions, e.g., location and culture; and 3) the relative temporal features of interactions and the time periods when the trust relationships hold. Most of the existing works only focus on some aspects of trust, and there is not a comprehensive study of user trust development that considers and incorporates 1)-3) in trust prediction. In this article, we propose a context-aware deep trust prediction model C-DeepTrust to fill this gap. First, we conduct user feature modeling to obtain the user's static and dynamic preference features in each context. Static user preference features are obtained from all the ratings and reviews that a user provided, while dynamic user preference features are obtained from the items rated/reviewed by the user in time series. The obtained context-aware user features are then combined and fed into the multilayer projection structure to further mine the context-aware latent features. Finally, the context-aware trust relationships between users are calculated by their context-aware feature vector cosine similarities according to the social homophily theory, which shows a pervasive property of social networks that trust relationships are more likely to be developed among similar people. Extensive experiments conducted on two real-world datasets show the superior performance of our approach compared with the representative baseline methods.

Hybrid Fuzzy Rule Algorithm and Trust Planning Mechanism for Robust Trust Management in IoT-Embedded Systems Integration

The Internet of Things (IoT) is rapidly expanding and becoming an integral part of daily life, increasing the potential for security threats such as malware or cyberattacks. Many embedded systems (ESs), responsible for handling sensitive data or facilitating secure online activities, must adhere to stringent security standards. For instance, payment processors employ security-critical components as distinct chips, maintaining physical separation from other network components to prevent the leakage of sensitive information such as cryptographic keys. Establishing a trusted environment in IoT and ESs, where interactions are based on the trust model of communication nodes, is a viable approach to enhance security in IoT and ESs. Although trust management (TM) has been extensively studied in distributed networks, IoT, and ESs, significant challenges remain for real-world implementation. In response, we propose a hybrid fuzzy rule algorithm (FRA) and trust planning mechanism (TPM), denoted FRA + TPM, for effective trust management and to bolster IoT and ESs reliability. The proposed system was evaluated against several conventional methods, yielding promising results: trust prediction accuracy (99%), energy consumption (53%), malicious node detection (98%), computation time (61 s), latency (1.7 ms), and throughput (9 Mbps).

S-DeepTrust: A deep trust prediction method based on sentiment polarity perception

In online social networks, trust prediction is crucial for applications such as user decision-making and high-impact user recommendation. In recent years, various solutions to the trust prediction problem have been proposed in many academic literatures. However, the existing methods only focus on using more available information to achieve better prediction performance, while ignoring the influence of the authenticity of user information and the user subjectivity. In this paper, we apply the sentiment labels to the trust prediction process for the first time, and propose a deep trust prediction method based on sentiment polarity perception, S-DeepTrust. We first use transfer learning to generate sentiment labels for user review data and obtain a sentiment polarity matrix. Next, we design a rating matrix generation algorithm, which weights the sentiment polarity matrix and the original rating matrix to produce a new rating matrix. Then, the Siamese network architecture is used for the first time in the field of trust prediction to judge the trust relationships based on the extracted user preference vectors, which minimizes the representation of trust user pairs. Finally, the experiment results show that our method achieves higher prediction accuracy and is robust to trust relation sparsity compared with existing methods.

A conflict elimination-based model for failure mode and effect analysis: A case application in medical waste management system

As a recognized and powerful risk analysis tool, failure mode and effects analysis (FMEA) has proven its effectiveness in medical waste management, which has profound significance to human health and ecological environment. However, existing FMEA studies pay little attention to the heterogeneity of team members (TMs) and their trust relationship, potentially leading to inappropriate results. Therefore, this paper constructs a FMEA model with internal and external TMs for medical waste management system, which focuses on the conflict elimination under social network to circumvent TMs’ negative behaviors and improve team harmony, thereby enhancing the reliability of FMEA. Firstly, to increase the flexibility and accuracy of information expression and synthesis, probabilistic linguistic term sets (PLTSs) and Dempster-Shafer evidence theory (D-S theory) are employed to provide and process evaluations of failure modes (FMs). Secondly, an objective trust prediction method based on bounded confidence is proposed for building a credible social network. Thirdly, a conflict elimination mechanism is devised to improve team cohesion, in which TM weights are dynamically determined. Afterwards, an integrated DEMATEL-TODIM decision framework is proposed to prioritize FMs. An application and related analysis prove that the proposed model can effectively improve the social relationship of TMs and obtain a reliable ranking of FMs on the basis of discriminating FMs’ causality while considering TMs’ bounded rationality.