Trust Model Research Articles

CCC-TM: Cross-Chain consensus committee method using a trust model

The presence of malicious nodes poses significant challenges to the robustness and security of blockchain cross-chain systems. This study investigates the interaction mechanism of blockchain cross-chain systems by integrating relay committee and trust models, aiming to mitigate the detrimental effects caused by malicious nodes on the overall system. Firstly, introducing a relay committee is proposed to facilitate the establishment of reliable relay nodes that serve as intermediaries connecting diverse blockchain networks. Secondly, the study devises a trust model, leveraging nodes’ past behaviors to evaluate their trustworthiness and identify and isolate potentially malicious nodes. Lastly, a mutual verification decentralized identifier (MVDID) enforces access control for inter-blockchain objects. In the experimental phase, the proportion of trusted nodes to the total number of nodes ranges from 0.1 to 0.3, yielding a consistently high transaction success rate between 97% and 100%. The findings indicate that implementing a relay committee enhances cross-chain operations’ efficiency and security. Additionally, the trust model aids in accurately identifying malicious nodes while promoting system transparency and collaboration. Consequently, this mechanism offers a comprehensive enhancement in performance and security for blockchain cross-chain systems.

Development of Trustworthiness for Cloud Service Providers Using DBN-Based Trust Model in Cloud Computing Environment

In order to facilitate diverse computing resources and services, cloud computing has evolve into a promising paradigm on-demand over the internet. To access services, cloud users have to rely on third-party service providers. Choosing a suitable Cloud Service Provider (CSP) with a raise in available cloud services in order to deliver the service safely is considered a serious concern for users. Regrettably, there are various problems that minimize the growth of cloud computing, like privacy, security loss, and control. The security issue is regarded as the most important element that could avoid the evolution of cloud computing. In the cloud environment, to handle the user’s requests, trust measures play a significant role when choosing appropriate service providers. Therefore, trustworthiness evaluation of CSP prior to choosing it to facilitate the service has become a significant obligation in cloud environment. In this work, a trust model, Deep Behavioral Feedback Quality of Service and Statistics based trust (Deep BFQS-trust), is developed to calculate trustworthiness of CSP based on its feedback and behavior, QoS and statistics-based given by the users. Also, to calculate behavioral trust values, various QoS attributes are considered. In order to maintain and calculate feedback trust value for service provider, diverse parameters from service level agreement are utilized. By computing the collective trust, trustworthiness of cloud service provider is judged that is computed by these trust factors. Moreover, the weights of the collective trust are determined by employing Deep belief network (DBN) model. Finally, the proposed Deep BFQS-trust technique is compared with the existing approaches, and exhibits that the proposed model attained utmost trustworthiness and successful interaction with the values of 0.860 and 0.888, respectively.

Cybersecurity Risks in Remote Work and Learning Environments and Methods of Combating Them

In a post-pandemic world, more and more people are opting to work from home or use personal devices for work and learning. These trends correspond to a greater rate of cyber attacks, as remote work makes it harder for companies to protect their employees and their data. In this paper, the cybersecurity risks of remote work are outlined, followed by possible means of protection and preventing attack. Some of the former include unsecured networks and expanded attack surfaces, while the latter includes enhanced endpoint management and the use of the Zero Trust model. However, it was concluded that the most important way to minimize cyber attacks in an age of remote work is to educate the public on the best security practices and the ways in which they can spot threats.

A Smart Secure model for Detection of DDoS Malicious Traces in Integrated LEO Satellite-Terrestrial Communications

For many researchers, defense against DDoS attacks has always been a major subject of attention. Within the LEO Satellite-Terrestrial (LSTN) network field, distributed denial of service (DDoS) attacks is considered to be one of the most potentially harmful attack techniques. For the facilitation of network protection by the detection of DDoS malicious traces inside a network of satellite devices, machine learning algorithms plays a significant role. This paper uses modern machine learning approaches on a novel benchmark Satellite dataset. The STIN and NSL-KDD datasets has been used to detect network anomalies. The pre-processing of data has been performed effectively and a host of ML methods have been applied to classify the outputs into normal, regular node or untrustable /malicious node. We have evaluated the analysis results in presence of attacks as well as without presence of attacks, supervised machine learning techniques basic measurements like accuracy, True positive, False positive etc. Our proposed trust model shows better accuracy, nearby 98% and we have shown that our proposed machine learning based security model performs better to get rid of DDoS attacks on integrated LEO satellite-terrestrial networks without compromising on the packet routing efficiency. We are able to improve routing speed and improve network security against distributed denial of service (DDoS) attacks by integrating an ensemble-based trust model trained on NSL-KDD+STIN+Exata Simulated resultant dataset with ACO for routing decisions. In dynamic network scenarios, as trustworthiness is an essential criterion in route decision-making, this proposed approach signifies resilient and adaptable routing.

Applying Trust Patterns to Model Complex Trustworthiness in the Internet of Things

Key aspects of communities of the Internet of Things (IoT) smart objects presenting social aspects are represented by trust and reputation relationships between the objects. Several trustworthiness models have been presented in the literature in the context of multi-smart object community that could be adopted in the IoT scenario; however, most of these approaches represent the different dimensions of trust using scalar measures, then integrating these measures in a global trustworthiness value. In this paper, we discuss the limitation of this approach in the IoT context, highlighting the necessity of modeling complex trust relationships that cannot be captured by a vector-based model, and we propose a new trust model in which the trust perceived by an object with respect to another object is modeled by a directed, weighted graph whose vertices are trust dimensions and whose arcs represent relationships between trust dimensions. By using this new model, we provide the IoT community with the possibility of representing also situations in which an object does not know a trust dimension, e.g., reliability, but it is able to derive it from another one, e.g., honesty. The introduced model can represent any trust structure of the type illustrated above, in which several trust dimensions are mutually dependent.

MULTIDIMENSIONAL MODEL FOR DIGITAL TRUST OF DIGITAL SERVICES USERS: RESULTS OF EMPIRICAL RESEARCH

The paper addresses the development of a multidimensional model of consumer trust of online services users, including such dimensions as trust in the effectiveness of regulatory activities of the state, consumer trust in online services, interpersonal trust, and predisposition to trust. Consumer trust studies in Russia are conducted primarily either in an offline context, or with an emphasis on only one of the dimensions of trust, at the same time Russian studies are mainly conceptual. The presented study is the first in Russia conducted using empirical tools and analyzing consumer trust as a multidimensional construct in an online context. The object of the empirical study is the users of online services - representatives of generations Y and Z, with the sample of the pilot study of 388 respondents, the Structural Equation Modeling method (PLS-SEM) used for analysis. The study reveals a significant influence of the predisposition to trust and the perceived effectiveness of the regulatory activities of the state on interpersonal trust. A significant positive influence of trust predisposition on the trust in online service is also confirmed, but no significant influence of the perceived effectiveness of the regulatory activity of the state on the trust in the online service was found. The study reveals a significant influence of interpersonal trust on trust in online services and the influence of trust in online services on consumers’ intention to use the service. The present effects of mediation and moderation are tested in the model. The presented research is relevant not only from a theoretical point of view, but also from a practical one, since the developed model allows us to identify the factors that form consumer trust in the online service. The findings demonstrate the perceived characteristics of the company which form consumer trust, and the factors that can reduce the negative impact of consumer distrust on the intention to use the service.

Trust interoperability in the Internet of Things

The Internet of Things (IoT) is a paradigm where entities or things are interconnected, often in heterogeneous contexts. As the interconnection happens, things establish collaborations with others, sometimes under uncertainty. Although trust can help us overcome this uncertainty, things might not be able to process the information about trust coming from other things: each thing could have its own trust model, which means its own way to understand and measure trust. If new trust relationships are to be established, it would be desirable to have a mechanism of interoperability that allows the things to process the information about the other things in terms of trust. In this paper, we describe an interoperability framework for tackling the trust interoperability issues in IoT, depending on the different types of trust models that might co-exist in the same IoT scenario.

Data Science's Unnoticed Significance in Finance

The growing influence of artificial intelligence and Data Science on the financial sector, focusing on its role in managing financial products and services, influencing financial markets, and shaping areas like internet finance, credit services, and fraud detection. The study highlights the opportunities and challenges of integrating data science into financial systems, including algorithmic trading of financial services. Despite challenges such as data quality, privacy, regulation, ethics, talent acquisition, and integration with existing systems, the application of data science in finance offers businesses opportunities to develop and broaden their offerings, fostering innovation and improving client experiences. The paper also emphasizes the importance of interpretable and explainable AI models for trust and compliance with regulations.

“It would work for me too”: How Online Communities Shape Software Developers’ Trust in AI-Powered Code Generation Tools

While revolutionary AI-powered code generation tools have been rising rapidly, we know little about how and how to help software developers form appropriate trust in those AI tools. Through a two-phase formative study, we investigate how online communities shape developers’ trust in AI tools and how we can leverage community features to facilitate appropriate user trust. Through interviewing 17 developers, we find that developers collectively make sense of AI tools using the experiences shared by community members and leverage community signals to evaluate AI suggestions. We then surface design opportunities and conduct 11 design probe sessions to explore the design space of using community features to support user trust in AI code generation systems. We synthesize our findings and extend an existing model of user trust in AI technologies with sociotechnical factors. We map out the design considerations for integrating user community into the AI code generation experience.

Initial development of perceptions of ability and intent factors of (un)trustworthiness in short‐term teams

SummaryIndividual perceptions of team trustworthiness are critical antecedents to the development of relationships within teams. Yet, clarity is needed regarding the focus of these individual perceptions and how they change, particularly at the outset of team interactions. Two distinct research streams have emerged regarding trustworthiness. One proposes a difference between perceived ability and intent, whereas the other proposes a difference between confident positive and negative expectations of others. Regardless of their structure, theory suggests that perceptions of team trustworthiness ought to develop across performance episodes as individuals identify more with their team and have more information about how they are performing. We explore these relationships across three distinct performance episodes in newly formed short‐term teams. Our results support a four‐factor model of trustworthiness within the context of short‐term, lab‐based teams. Further, perceptions regarding teammates' abilities tended to change quickly and curvilinearly while those regarding teammates' intents changed linearly. Positive team performance signals bolstered individual perceptions of team trustworthiness in ability and intent but not perceived team untrustworthiness. Negative team performance signals bolstered individual perceptions of team untrustworthiness in ability and intent while harming both types of individual perceptions of team trustworthiness. This study supports propositions from the model of trust over time and lays the groundwork for a comprehensive approach toward trust research within teams.

Multi-TA model-based conditional privacy-preserving authentication protocol for fog-enabled VANET

The rapid growth of Vehicular Ad-hoc Networks (VANET), fueled by advancements in the Internet-of-Things, cloud computing, Intelligent Transportation Systems, and fog computing, has led to the introduction of fog node-based VANET to serve resource-constrained devices. In the traditional security models of VANET, due to the use of a centralized trusted authority, there is a chance of single-point-of-failure and service unavailable with the increased service access requests. Also, there was one-to-one communication between each roadside unit and trusted authority. This may increase the system complexity and increase the traffic load. To address these issues, a novel authentication protocol for fog-enabled VANET based on multiple trusted authority model is discussed which reduces the chance of service unavailability and single-point-of-failure as the entire traffic load is distributed among multiple sub-trusted authority. Due to the incorporation of fog node, a group of roadside units can be controlled centrally, where trusted authority does not need to perform individual authentication for each roadside unit. The proposed protocol's security is rigorously examined through both informal and formal security analysis. Additionally, the protocol exhibits enhanced security features, as demonstrated in a performance comparison section, showcasing its ability to meet the security and privacy requirements while incurring relatively low communication and computation and storage costs. Thus, the proposed protocol offers a secure and efficient authentication protocol for fog-enabled VANET.

Analysis of the Prospects and Development of Insurance Premium Trust Products in Hong Kong

Insurance premium trust is a hybrid model that combines insurance and trust services. In recent years, with the gradual improvement of China’s economy, there has been a heightened awareness of wealth management among people. To meet this growing demand, the insurance market has expanded greatly, resulting in the emergence of various new insurance products, including insurance premium trust products. Based on the status of the domestic insurance market and Hong Kong insurance market in recent years, this paper summarizes the advantages and disadvantages of insurance premium trust products launched by the Hong Kong insurance industry. Furthermore, it discusses the prospects and development of the Hong Kong insurance premium trust model against the background of the establishment of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA).

Trustworthiness of Policymakers, Technology Developers, and Media Organizations Involved in Introducing AI for Autonomous Vehicles: A Public Perspective

Qualities of organizations constitute dimensions of trustworthiness. Guided by the integrative model of organizational trust, we developed dimensions of trustworthiness of policymakers, technology developers, and media organizations that are involved in introducing artificial intelligence for autonomous vehicles. We collected data through six focus group discussions with the public in Singapore. In addition to the core dimensions of trustworthiness, the public would consider acclaim, collaboration, public communication, and affiliation. Further, we identified all the dimensions of trustworthiness as either ability-, recognition-, relation-, or principle-based. These findings carry important implications for the development of the model and stakeholders’ communication about science and technology.

Implementation of Trust Model for Social Internet of Things Using Centrality Feed Forward Networks

Recent advances of Internet of Things (IoT) lead to the most promising paradigm called Social Internet of Things (SIoT). These techniques are considered the strong amalgamation of the social networking features with the IoT objects. These networks are characterized by facilitating the IoT objects to establish the social networking between each other. In SIoT, an interconnection of networks formed by considering the important features such a object-object interactions, social relationship, reliable recommendations and mandates the careful attention towards the strong trustworthy connections. Hence Intelligent Trust Model Identification Model System (ITMIS) is required for the SIoT networks to identify the misbehaving objects by selecting only the reliable, credible and trustworthy objects before relying on the services provided by them. However, existing frameworks truly relay on the conventional approaches that is based on linear relationship between the inputs and outputs. These methods may lead to the high misclassification ratio of selecting the untrusted devices that even may cause untrust process in an application. To overcome this problem, in the proposed research work the novel ITMIS which ensembles the non-linear centralities relationships with the architecture Extreme Feed Forward Neural Networks (EFFNN) with the combination of hybrid algorithm of Long Short Term Memory (LSTM) and Gated Recuurent Unit (GRU) for the better accuracy compare with the existing model. The proposed model captures the number of key trust metrics based on centralities measurements and envisages the EFNN to classify the trusty and non-trust objects. The extensive experimentations are conducted using the real world datasets and various trust metrics were evaluated and compared with the other state-of-the-art trust learning models. The results demonstrate that the proposed model has outperformed with the other existing models by maintaining the accuracy of 95% to 94% with the decreasing untrusted rates and It illustrates conclusively that the LSTM's use of enhancing ensemble characteristics has shown to be more advantageous. While other models, like E-LSTM (90%) to 80%, Stacked LSTM (85% to 80%), SVM (85% to 79%), KNN (75% to 68%), and RF (65% to 58%), exhibit decreasing efficiency, the proposed approach illustrates that it is more productive as well as efficient for building an intelligent trust classification system that is appropriate for establishing trusted SIoT network communication.

A Federated Deep Reinforcement Learning-Based Trust Model in Underwater Acoustic Sensor Networks

A Federated Deep Reinforcement Learning-Based Trust Model in Underwater Acoustic Sensor Networks

Evaluating Trust Management Frameworks for Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) are crucial in various fields including Health Care Monitoring, Battlefield Surveillance, and Smart Agriculture. However, WSNs are susceptible to malicious attacks due to the massive quantity of sensors within them. Hence, there is a demand for a trust evaluation framework within WSNs to function as a secure system, to identify and isolate malicious or faulty sensor nodes. This information can be leveraged by neighboring nodes, to prevent collaboration in tasks like data aggregation and forwarding. While numerous trust frameworks have been suggested in the literature to assess trust scores and examine the reliability of sensors through direct and indirect communications, implementing these trust evaluation criteria is challenging due to the intricate nature of the trust evaluation process and the limited availability of datasets. This research conducts a novel comparative analysis of three trust management models: "Lightweight Trust Management based on Bayesian and Entropy (LTMBE)", "Beta-based Trust and Reputation Evaluation System (BTRES)", and "Lightweight and Dependable Trust System (LDTS)". To assess the practicality of these trust management models, we compare and examine their performance in multiple scenarios. Additionally, we assess and compare how well the trust management approaches perform in response to two significant cyber-attacks. Based on the experimental comparative analysis, it can be inferred that the LTMBE model is optimal for WSN applications emphasizing high energy efficiency, while the BTRES model is most suitable for WSN applications prioritizing critical security measures. The conducted empirical comparative analysis can act as a benchmark for upcoming research on trust evaluation frameworks for WSNs.

An Adaptive Trust Evaluation Model for Detecting Abnormal Nodes in Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks have a wide range of applications in both civil and military fields, but the complex and changing underwater environment makes them vulnerable to multiple security threats. Trust mechanisms are effective ways to enhance network security and reliability. In order to improve the accuracy of trust evaluation and the detection rate of abnormal nodes, this paper proposes an adaptive trust evaluation model based on fuzzy logic. This model adopts a variable weight fuzzy comprehensive evaluation algorithm to dynamically adjust the weights of three direct trust indicators to ensure the accuracy of direct trust evaluation. Then, it uses fuzzy closeness to eliminate unreliable recommendation trust and adjusts the weight of recommendation trust through deviation to improve the accuracy of indirect trust. The simulation results show that the model can effectively improve the accuracy of trust evaluation and the detection rate of abnormal nodes. Especially when the link quality is unstable, the success rate of detecting abnormal nodes in this model is improved by more than 10% compared with the existing trust model.

Third-Party and Bankruptcy Effects under Chinese Trust Law: Comparisons with English Trust Law

Abstract In English law, the trust's third-party and bankruptcy effects contribute significantly to its wide use in commercial transactions. In view of the trust's attractiveness in conducting commercial dealings, China has also introduced the trust model into its domestic legal system to enhance its financial infrastructure. However, given the extent to which Chinese law has been influenced by the Roman-Germanic tradition, China's replication of the trust's third-party and bankruptcy effects has encountered doctrinal obstacles. Drawing upon the experience of its Northeast Asian forerunners, China has established two mechanisms to achieve the third-party and bankruptcy effects: the regime of trust fund independence and the granting of the right of rescission to beneficiaries. These two mechanisms represent the adjustments made by Chinese legislators in the process of transplanting the trust model into the Chinese legal context. Adopting a comparative law perspective, this article examines these mechanisms in the Chinese law setting for two reasons: first, to explore the mechanisms’ constituent elements and their operation, as well as the roles of both mechanisms in the Chinese trust law system; and second, to furnish comparative law scholarship with broader insights into rule transplantation and reconciliation.

The Need of Trustworthy Announcements to Achieve Driving Comfort

An Intelligent Transport System (ITS) is more demanding nowadays and it can be achieved through deploying Vehicular Ad Hoc Networks (VANETs). Vehicles and Roadside Units (RSUs) exchange traffic events. Malicious drivers generate false events. Thus, they need to be identified to maintain trustworthy communication. When an authorised user acts maliciously, the security scheme typically fails. However, a trust model can isolate false messages. In this paper, the significance of trustworthy announcements for VANETs is analysed. To this end, a series of experiments is conducted in Veins to illustrate how the trustworthiness of announcements affects travel time. A traffic scenario is created where vehicles detour to an alternate route with an announcement from the leading vehicle. Both true and false announcements are considered. Results confirm that false announcements and refraining from announcements increase travel time. However, the travel time is reduced with trustworthy announcements. From this analysis, it can be concluded that trustworthy announcements facilitate driver comfort.

Implementation of Trust Model for Social Internet of Things Using Centrality Feed Forward Networks

Recent advances of Internet of Things (IoT) lead to the most promising paradigm called Social Internet of Things (SIoT). These techniques are considered the strong amalgamation of the social networking features with the IoT objects. These networks are characterized by facilitating the IoT objects to establish the social networking between each other. In SIoT, an interconnection of networks formed by considering the important features such a object-object interactions, social relationship, reliable recommendations and mandates the careful attention towards the strong trustworthy connections. Hence Intelligent Trust Model Identification Model System (ITMIS) is required for the SIoT networks to identify the misbehaving objects by selecting only the reliable, credible and trustworthy objects before relying on the services provided by them. However, existing frameworks truly relay on the conventional approaches that is based on linear relationship between the inputs and outputs. These methods may lead to the high misclassification ratio of selecting the untrusted devices that even may cause untrust process in an application. To overcome this problem, in the proposed research work the novel ITMIS which ensembles the non-linear centralities relationships with the architecture Extreme Feed Forward Neural Networks (EFFNN) with the combination of hybrid algorithm of Long Short Term Memory (LSTM) and Gated Recuurent Unit (GRU) for the better accuracy compare with the existing model. The proposed model captures the number of key trust metrics based on centralities measurements and envisages the EFNN to classify the trusty and non-trust objects. The extensive experimentations are conducted using the real world datasets and various trust metrics were evaluated and compared with the other state-of-the-art trust learning models. The results demonstrate that the proposed model has outperformed with the other existing models by maintaining the accuracy of 95% to 94% with the decreasing untrusted rates and It illustrates conclusively that the LSTM's use of enhancing ensemble characteristics has shown to be more advantageous. While other models, like E-LSTM (90%) to 80%, Stacked LSTM (85% to 80%), SVM (85% to 79%), KNN (75% to 68%), and RF (65% to 58%), exhibit decreasing efficiency, the proposed approach illustrates that it is more productive as well as efficient for building an intelligent trust classification system that is appropriate for establishing trusted SIoT network communication.