Trust Model Research Articles

Trust Modelling and Management for IoT Healthcare

Trust Modelling and Management for IoT Healthcare

A Blockchain-based Trust and Reputation Model with Dynamic Evaluation Mechanism for IoT

The rapid development of the Internet of Things (IoT) has dramatically increased the number of distributed IoT devices and users. Trust and Reputation Model (TRM) is a well-known technique for improving the security of IoT, which detects malicious attacks by evaluating user behavior. Since traditional distributed TRMs lack secure and reliable data sharing mechanisms, some works have integrated the TRMs into the trusted blockchains. Nevertheless, they have not realized the security requirements of the comprehensive assessment of user behavior and dynamic evaluation of reputation. Therefore, this paper introduces a Blockchain-based Trust and Reputation Model (BTRM), which evaluates user reputation from many aspects and can resist multiple malicious attacks in the distributed network. Second, we propose a novel Dynamic Evaluation Mechanism (DEM), which reduces the number of reputation evaluations without degrading network security and builds a trusting foundation between long-term inactive users and the network. Eventually, we deploy the proposed model DEM-BTRM in a prototype system of Hyperledger Fabric and compare it with existing reputation evaluation methods. The results show that the DEM-BTRM can comprehensively evaluate user behavior and dynamically detect malicious attacks.

Mitigating Black Hole Attacks in Routing Protocols Using a Machine Learning-Based Trust Model

Many application domains gain considerable advantages with the internet of things (IoT) network. It improves our lifestyle towards smartness in smart devices. IoT devices are mostly resource-constrained such as memory, battery, etc. So it is highly vulnerable to security attacks. Traditional security mechanisms can't be applied to these devices due to their restricted resources. A trust-based security mechanism plays an important role to ensure security in the IoT environment because it consumes only fewer resources. Thus, it is essential to evaluate the trustworthiness among IoT devices. The proposed model improves trusted routing in the IoT environment by detecting and isolating malicious nodes. This model uses reinforcement learning (RL) where the agent learns the behavior of the node and isolates the malicious nodes to improve the network performance. The model focuses on IoT with the routing protocol for low power and lossy network (RPL) and counters the blackhole attack.

Research on Information Leakage Tracking Algorithms in Online Social Networks

An online social network is a platform where people can communicate with friends, share information, speed up business development, and improve teamwork. A large amount of user privacy information existing in real social networks is leaked from person to person, and this issue has hardly been studied. With the rapid expansion of the network, the issue of privacy protection has received increasing attention. So far, many privacy protection methods including differential protection algorithms, encryption algorithms, access control strategies, and anonymization have been researched and applied. Information leakage means that the information shared by the user is disseminated or downloaded by his friends without the user's consent, and the transmission of private information will not be recorded. In order to track and find out the ways and methods of information leakage, this article adopts an unusual method, namely, the probability judgment based on trust. By screening the similarities between users, past information exchanges, and the topology of social networks, a trust model is established to evaluate and estimate the degree of trust between users. According to the rating information privacy of friends' trust, an information dissemination system is established, which can be applied to online social networking platforms to reduce the risk of information leakage, thereby ensuring the security of users' private information. At the same time, this paper expands the transmission system model without user authorization and proposes a fingerprint-based deterministic leak tracking algorithm.

A node trust evaluation method of vehicle-road-cloud collaborative system based on federated learning

As the vehicle-road-cloud collaboration system develops rapidly, it is accompanied by serious information security problems while solving the data transmission issues. For constructing secure transmission of data, trust is recommended as a relevant way to accomplish network security; that is, developing a trust model that can be used by sensor nodes to determine the reliability of another node is crucial. However, the heterogeneity of the network has different functional requirements for trust evaluation, and the openness of the network makes the nodes more vulnerable to attacks. Therefore, the research of trust evaluation model in the vehicle-road-cloud collaborative system is facing greater challenges than the traditional network. In this paper, a trust evaluation scheme of the vehicle-road-cloud collaborative system based on Federated Learning (FLT) is proposed. A hierarchical trust evaluation model is designed, and the complex model is simplified to an orderly hierarchical structure by using hierarchical analysis. The trust indexes of different layers are evaluated, and the influencing factors among different nodes are comprehensively considered. Combined with federated learning, it solves the problem of finding the most reliable route and realizes personalization at the level of equipment, data, and model. For the purpose of alleviating the heterogeneity and obtaining a high-quality personalized model for each device, trust values can be adaptively updated as changes in the topology of the network occur in real-time. The simulation findings demonstrate that, as compared to previous schemes, the energy consumption is lowered by 35%, and the accuracy is raised by 45% while maintaining trust stability.

Comparative Analysis of Different Trust Metrics of User-User Trust-Based Recommendation System

Information overload is the biggest challenge nowadays for any website, especially e-commerce websites. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations such as data sparsity, low accuracy, cold-start, and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the study aims to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes twenty-four trust metrics in terms of the methodology, trust properties \& measurement, validation approaches, and the experimented dataset.

SoK: Privacy-enhancing Smart Home Hubs

Smart homes are IoT systems enabling the automation of household operation. The unrestricted collection and processing of data by smart home systems raises legitimate privacy concerns for their users. Over the past decade, there has been significant interest in privacy-enhancing technologies applied at the level of a local smart hub physically located in the home and acting as a gateway between sensors, applications, platform providers, and services in the cloud. The number and variety of projects and research proposals can, however, make their comparison a daunting and unnecessarily complex task. We systematize existing knowledge in this field through the analysis and categorization of 10 industrial and community-contributed systems and 37 research proposals from the literature of the past 11 years. Our results shed light on the diversity of system and trust models considered in the state-of-the-art and on the associated privacy-enhancing technologies. We further identify open research problems and promising approaches that would benefit the smart home hub model and the protection of smart home users’ privacy.

Distributed Trust and Reputation Management for Future Wireless Systems

Trust lies at the center of the paradigm shift required to realize the ultra-dense networks needed by future radio communication systems. We propose a distributed, three-layer, trust-based hardware sharing scheme between operators that overcomes the limitations of a single-operator-owned monolithic network. Our system scales to tens of thousands of operators without requiring explicit contracts between them, or between operators and user equipment. User equipment in turn is free to requisition the services of any available hardware. This is achieved by abstracting the communication process as a transaction, and casting it within a distributed ledger technology framework paired with an efficient, fault-tolerant, distributed consensus protocol. A trust model associates a behavioral measure with each hardware device that signals its reliability, as well as its payoff. The proposed system offers multiple advantages for users, operators, and regulators.

Weighted Coefficient Firefly Optimization Algorithm and Support Vector Machine for Trust Model and Link Reliability

Cloud computing is widely used by organizations and individuals due to its flexibility and reliability. The trust model is important for cloud computing to detect malicious users and protect user privacy. The existing research faces the issues of local optima trap and overfitting problems when a training user node is idle for more time. This research proposed Weighted Coefficient Firefly Optimization Algorithm (WCFOA) with Support Vector Machine (SVM) for the trust model calculation and identifying paths with better Quality of Services (QoS). The weighted coefficient is added to the FOA model to balance the exploration and exploitation in the search of identifying optimal path based on reliability score. The WC-FOA method measures the link reliability in the model and SVM detects the malicious users in the model. The WC-FOA model selects the optimal path for transmission in terms of trust and efficient QoS parameters. The entropy measure and link reliability are provided as input to the SVM model for the detection of attacks in the network. The WCFOA-SVM model has 96% malicious user detection, whereas the Random Forest Hierarchical Ant Colony Optimization (RF-HEACO) has 92 % accuracy.

Promoting collaborative learning in virtual worlds: the power of “we”

Purpose As digital spaces for team collaboration, virtual worlds bring considerable verisimilitude to technology-mediated social interaction and change the process of traditional team learning. The purpose of this study is to understand how to promote collaborative learning in virtual worlds by leveraging the power of we-intention to participate in virtual worlds. The authors further use the valence–instrumentality–self-efficacy–trust model (VIST) model as a means of understanding the formation of we-intention to participate in virtual worlds, during which behavioral desire serves a bridging role.Design/methodology/approach The authors tested the research model using the data gathered from 298 users of a prominent form of virtual world, i.e. massively multiplayer online role-playing games. The authors used the structural equation modeling approach and the partial least squares technique for data analysis.Findings Results show that the four factors of the VIST model (i.e. valence on team goals, instrumentality of contribution, self-efficacy in team tasks and trust in team members) all positively influence we-intention to participate in virtual worlds through behavioral desire for team actions. We-intention to participate in virtual worlds further exerts a stronger positive effect on collaborative learning in virtual worlds, compared with I-intention to participate in virtual worlds.Originality/value This work advances the information systems literature by introducing a relevant and important concept, i.e. we-intention, to explain collaborative learning in virtual worlds. This study especially compared the effect of we-intention and I-intention on collaborative learning in virtual worlds. The results of this work also provide practitioners with insights into the role of we-intention in promoting collective actions in virtual worlds.

Research on a Dynamic Trusted Model Based on Time-sharing Control

In this paper, a high-availability time-sharing dynamic trusted model is proposed, which controls the access of untrusted domains to trusted domains by time-sharing, dynamically adjusts the trusted domain and untrusted domains, and proves their security with non-interference theory, which improves the usability of the dynamic trusted model.

Explicit and Implicit Trust Modeling for Recommendation

Recommendation has become an inseparable component of many software applications, such as e-commerce, social media and gaming platforms. Particularly in collaborative filtering-based recommendation solutions, the preferences of other users are considered heavily. At this point, trust among the users comes into the scene as an important concept to improve the recommendation performance. Trust describes the nature and the strength of ties between individuals and hence provides useful information to improve the recommendation accuracy, particularly against data sparsity and cold start problems. The Trust notion helps alleviate the effect of these problems by providing additional reliable relationships between the users. However, trust information, specifically explicit trust, is not straightforward to collect and is only scarcely available. Therefore, implicit trust models have been proposed to fill in the gap. The literature includes a variety of studies proposing the use of trust for recommendation. In this work, two specific sub-problems are elaborated on: the relationship between explicit and implicit trust scores, and the construction of a machine learning model for explicit trust. For the first sub-problem, an implicit trust model is devised and the compatibility of implicit trust scores with explicit scores is analyzed. For the second sub-problem, two different explicit trust models are proposed: Explicit trust modeling through users’ rating behavior and explicit trust modeling as a link prediction problem. The performances of the prediction models are analyzed on a set of benchmark data sets. It is observed that explicit and implicit trust models have different natures, and are to be used in a complementary way for recommendation. Another important result is that the accuracy of the machine learning models for explicit trust is promising and depends on the availability of data.

A Blockchain-Based Trust Model for Uploading Illegal Data Identification

Malicious users can upload illegal data to the blockchain to spread it, resulting in serious threats due to the tamper-proof characteristics of the blockchain. However, the existing methods for uploading illegal data identification cannot select trust nodes and ensure the credibility of the identification results, leading to a decrease in the credibility of the methods. To solve the problem, this paper proposes a blockchain-based trust model for uploading illegal data identification. The trust model mainly has the following two core modules: Reputation-based random selection algorithm (RBRSA) and incentive mechanism. By assigning reputation attributes to nodes, the proposed RBRSA will select nodes according to reputation values. RBRSA favors the nodes with high reputation value to ensure the randomness and credibility of the identification nodes. The incentive mechanism is designed to ensure the credibility of the identification results through the credibility analysis of the model based on game theory and Nash equilibrium. Identification nodes that identify illegal data correctly will obtain incentives. In order to obtain a higher income, the identification nodes must identify illegal data correctly. Credibility analysis and comparative experiments show that the probability of selecting credible nodes by RBRSA is up to 23% higher than the random selection algorithm. The probability of selecting the nodes with a reputation value of 20 by RBRSA is 27% lower than the random selection algorithm; that is, the probability that RBRSA selects untrusted nodes is lower. Therefore, the nodes selected by RBRSA have superior credibility compared with other methods. In terms of the effect of the incentive mechanism, the incentive mechanism can encourage nodes to identify data credibly and improve the credibility of identification results. All in all, the trusted model has higher credibility than other methods.

How Managers Make Sense of Human Resource Management’s Role in Building Trust: Enacting Espoused Human Resource Management in Indian Gas and Petrol Public Sector Organisations

The desire to mobilise effective strategic human resource management in India’s new public management domain has seen the role of organisational trust receive greater scholarly and practical scrutiny. This study explores managers’ perception of HRM’s role in building organisational trust in five public sector organisations in India using exploratory multiple case studies. In implementing HRM, the findings suggest that managers are cognisant of specific human resource practices that can be socially adapted to enhance their effectiveness. Implications from the study’s emergent process model of human resource management and trust highlight the cross-level influences that affect India’s public sector organisations’ outcomes.

SLA-trust-energy aware path computation for critical services in Blockchain-enabled intelligent transport system

The innovation in the Internet of Things (IoT) has amplified numerous time-bounded critical applications in different areas such as healthcare, banking, industries, product delivery services, and online food services. In this paper, an effort has been made an effort to propose a novel framework for critical services in Intelligent Transport systems (ITS). In ITS, critical services involve activities such as the exchange of information or transmission of data among vehicles, accessing of information in the case of accidents, determination of the best-routed route from the current location to a hospital, and determination of a low-congestion traffic route during working hours. The unavailability of any information leads to considerable loss of business, life, or property. This study aims to quantify the transportation service assurance by conceptualizing Service Level Agreements (SLAs) for various critical services, the energy requirement for complete data transmission, and providing Blockchain-enabled secured data transmission by calculating trust metrics of IoT nodes using a trust analyzer. In conclusion, the proposed mechanism is evaluated against various routing parameters by quantifying mean satisfied routes in ITS, throughput, delay, energy efficiency, trust in data transmission, and results with security configurations for tolerance toward attacks.

A Distributed Model for Privacy Preserving V2I Communication with Strong Unframeability and Efficient Revocation

Although Vehicle to Infrastructure (V2I) communications greatly improve the efficiency of early warning systems for car safety, communication privacy is an important concern. Although solutions exist in the literature for privacy preserving VANET communications, they usually require high trust assumptions for a single authority. In this paper we propose a distributed trust model for privacy preserving V2I communications. Trust is distributed among a certification authority that issues the vehicles’ credentials, and a signing authority that anonymously authenticates V2I messages in a zero knowledge manner. Anonymity is based on bilinear pairings and partially blind signatures. In addition, our system supports enhanced conditional privacy since both authorities and the relevant RSU need to collaborate to trace a message back to a vehicle, while efficient certificateless revocation is supported. Moreover, our scheme provides strong unframeability for honest vehicles. Even if all the entities collude, it is not possible to frame a honest vehicle, by tracing a forged message back to an honest vehicle. The proposed scheme concurrently achieves conditional privacy and strong unframeabilty for vehicles, without assuming a fully trusted authority. Our evaluation results show that the system allows RSUs to efficiently handle multiple messages per second, which suffices for real world implementations.

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

The exposure of IoT nodes to the internet makes them vulnerable to malicious attacks and failures. These failures affect the survivability, integrity, and connectivity of the network. Thus, the detection and elimination of attacks in a timely manner become an important factor to maintain network connectivity. Trust-based techniques are used in understanding the behavior of nodes in the network. The proposed conventional trust models are power-hungry and demand large storage space. Succeeding this Hidden Markov Models have also been developed to calculate trust but the survivability of the network achieved from them is low. To improve survivability, selfish and malicious nodes present in the network are required to be treated separately. Hence, in this paper, an improved Hidden Markov Trust (HMT) model is developed, which accurately detects the selfish and malicious nodes that illegally intercept the network. The proposed model comprises the Learning Module which aims to understand the behavior of nodes and compute trust using HMT with the expected output. The probability parameters of the HMT model are derived from the data flow rate and the residual energy of the nodes. Next, in Decision-Module, the actual nature of the node is obtained with the help of the evaluated node’s likelihood functions. If the node is selfish and is close to crashed state then, is isolated from the routing function, while the selfish node with sufficient energy is immediately destroyed from the network. On the other hand, malicious nodes are provided with a time-based opportunity to reset themselves before being knocked down. Finally, if the node is legitimate, then the function continues smoothly. At last, the Path-Formation-Module establishes the trusted optimal routing path. Further, comparative analysis for attacks such as black-hole, grey-hole, and sink-hole has been done and performance parameters have been extended to survivability-rate, power consumption, delay, and false-alarm-rate, for different network sizes and vulnerability. Simulation result on average provides a 10% higher PDR, 29% lower overhead, and 17% higher detection rate when compared to a Futuristic Cooperation Evaluation Model, Futuristic Trust Coefficient-based Semi-Markov Prediction Model, Opportunistic Data Forwarding Mechanism, and Priority-based Trust Efficient Routing using Ant Colony Optimization trust models presented in the literature.

RouteChain: Towards Blockchain-based secure and efficient BGP routing

Routing on the Internet is defined among autonomous systems (ASes) based on a weak trust model where it is assumed that ASes are honest. While this trust model strengthens the connectivity among ASes, it also results in an attack surface that can be exploited to hijack the routing paths. One such attack is known as the BGP prefix hijacking, in which a malicious AS broadcasts IP prefixes that belong to a target AS, thereby hijacking its traffic. In this paper, we propose RouteChain: a blockchain-based secure BGP routing system that counters BGP hijacking and maintains a consistent view of the Internet routing paths. Towards that, we leverage provenance assurance and tamper-proof properties of blockchains to augment trust among ASes. We group ASes based on their geographical (network) proximity and construct a bi-hierarchical blockchain model that detects false prefixes prior to their spread over the Internet. We evaluate RouteChain using three different consensus protocols and show its effectiveness by drawing a case study with the Youtube hijacking of 2008. Our results show that RouteChain can efficiently detect false prefix announcements and prevent BGP attacks over the Internet.

Approaches and Challenges in Internet of Robotic Things

The Internet of robotic things (IoRT) is the combination of different technologies including cloud computing, robots, Internet of things (IoT), artificial intelligence (AI), and machine learning (ML). IoRT plays a major role in manufacturing, healthcare, security, and transport. IoRT can speed up human development by a very significant percentage. IoRT allows robots to transmit and receive data to and from other devices and users. In this paper, IoRT is reviewed in terms of the related techniques, architectures, and abilities. Consequently, the related research challenges are presented. IoRT architectures are vital in the design of robotic systems and robotic things. The existing 3–7-tier IoRT architectures are studied. Subsequently, a detailed IoRT architecture is proposed. Robotic technologies provide the means to increase the performance and capabilities of the user, product, or process. However, robotic technologies are vulnerable to attacks on data security. Trust-based and encryption-based mechanisms can be used for secure communication among robotic things. A security method is recommended to provide a secure and trustworthy data-sharing mechanism in IoRT. Significant security challenges are also discussed. Several known attacks on ad hoc networks are illustrated. Threat models ensure integrity confidentiality and availability of the data. In a network, trust models are used to boost a system’s security. Trust models and IoRT networks play a key role in obtaining a steady and nonvulnerable configuration in the network. In IoRT, remote server access results in remote software updates of robotic things. To study navigation strategies, navigation using fuzzy logic, probabilistic roadmap algorithms, laser scan matching algorithms, heuristic functions, bumper events, and vision-based navigation techniques are considered. Using the given research challenges, future researchers can get contemporary ideas of IoRT implementation in the real world.

Islamic social financing and efficient zakat distribution: impact of fintech adoption among the asnaf in Malaysia

PurposeThe purpose of this study is to investigate the impact factors towards the use of mobile banking among the asnaf for efficient zakat payments by using the unified theory of acceptance and use of technology (UTAUT2) model with additional constructs such as religiosity, attitude and trust (initial trust model) in the Malaysian context.Design/methodology/approachThe structural equation modelling is used to analyse the data collected from 470 asnaf in Selangor, Malaysia, using the survey methodology.FindingsThis study highlights factors that could influence the asnaf to adopt financial technology (fintech) in order for them to enjoy the benefit of efficient zakat distribution by the zakat institutions.Research limitations/implicationsThis study only investigates and contributes added value from the Malaysian context. While it may be similar to other countries that share similar demographic profiles, it might differ for countries that do not have any demographic similarities with Malaysia.Practical implicationsThe result of this study highlights the significant factor that practitioners (zakat institutions) in Malaysia could improve fintech adoption and at the same time enhance the efficiency of zakat distribution for the asnaf.Social implicationsThis research is basically addressing Islamic social financing. Thus, this study has social implications to address the society's poverty eradication scheme pertaining to the efficiency of zakat institutions in Malaysia for the speedier zakat payments to the asnaf. However, other emerging issues also arise particularly in using the zakat funds by the zakat institutions. By right whatever expenditure that the institutions use from the zakat funds need to have the consent from the asnaf because of their religious and legal rights. Hence, this new gap found from the findings of this study could be further explored by other researchers in future studies especially focusing on the drive towards more efficient administration by the zakat institutions of Malaysia in particular and other zakat institutions spread over Muslim countries elsewhere.Originality/valueThis study found that the UTAUT2 model is rarely used and adopted in investigating technology acceptance and adoption especially when it is related to religious matters such as zakat. Indeed, the findings of this study could be acknowledged as something of original value and significance, therefore contributes to the body of knowledge in this field.