Trust Model Research Articles

Overcome Medical Algorithm Aversion: Conditional Joint Effect of Direct and Indirect Information

The just-ended COVID-19 pandemic and the looming global aging remind us that we need to be prepared for the shortage of doctors, which might become an urgent medical crisis in the future. Medical AI could relieve this urgency and sometimes perform better than human doctors; however, people are reluctant to trust medical AI because of algorithm aversion. Although several factors that can minimize algorithm aversion have been identified, they are not effective enough to promote medical AI as people’s first choice. Therefore, inspired by the direct and indirect information model of trust and media equation hypothesis, this research explored a new method to minimize aversion to medical AI by highlighting its social attributes. In 3 between-subject studies, a medical AI system’s direct information (i.e., transparency and quantitation of the decision-making process (DMP)) and indirect information (i.e., social proof) were manipulated. Study 1 (N = 193) and 2 (N = 429) showed that transparency of DMP and social proof increased trust in AI, but did not affect trust in human doctors. Social proof jointly affected trust in AI with non-quantitative DMP but not quantitative DMP. Study 3 (N = 184) further revealed the joint effect of the transparent non-quantitative DMP and near-perfect social proof, which could minimize algorithm aversion. These results extended the direct-indirect information model in interpersonal trust, revealed conditional media equation in human-AI trust, and offered practical implications for medical AI interface design.

AI-powered malware detection with Differential Privacy for zero trust security in Internet of Things networks

The widespread usage of Android-powered devices in the Internet of Things (IoT) makes them susceptible to evolving cybersecurity threats. Most healthcare devices in IoT networks, such as smart watches, smart thermometers, biosensors, and more, are powered by the Android operating system, where preserving the privacy of user-sensitive data is of utmost importance. Detecting Android malware is thus vital for protecting sensitive information and ensuring the reliability of IoT networks. This article focuses on AI-enabled Android malware detection for improving zero trust security in IoT networks, which requires Android applications to be verified and authenticated before providing access to network resources. The zero trust security model requires strict identity verification for every entity trying to access resources on a private network, regardless of whether they are inside or outside the network perimeter. Our proposed solution, DP-RFECV-FNN, an innovative approach to Android malware detection that employs Differential Privacy (DP) within a Feedforward Neural Network (FNN) designed for IoT networks under the zero trust model. By integrating DP, we ensure the confidentiality of data during the detection process, setting a new standard for privacy in cybersecurity solutions. By combining the strengths of DP and zero trust security with the powerful learning capacity of the FNN, DP-RFECV-FNN demonstrates the ability to identify both known and novel malware types and achieves higher accuracy while maintaining strict privacy controls compared with recent papers. DP-RFECV-FNN achieves an accuracy ranging from 97.78% to 99.21% while utilizing static features and 93.49% to 94.36% for dynamic features of Android applications to detect whether it is malware or benign. These results are achieved under varying privacy budgets, ranging from ϵ=0.1 to ϵ=1.0. Furthermore, our proposed feature selection pipeline enables us to outperform the state-of-the-art by significantly reducing the number of selected features and training time while improving accuracy. To the best of our knowledge, this is the first work to categorize Android malware based on both static and dynamic features through a privacy-preserving neural network model.

The component and structure of interpersonal trust

Prior research has identified trust trait, trust expectation, trust risk and trust behavior as integral components of interpersonal trust. However, there still lack an in-depth exploration of the structural relationships among these integral components—how these integral components collectively constitute interpersonal trust. The current study innovatively proposed that interpersonal trust is anchored by individual trust trait, mediated by the dynamic equilibrium between trust risk and trust expectation, and culminates in trust behavior as the outcome. Interpersonal trust results from the synergistic interplay of individual and environmental factors. We called such structural relationships as the pyramid structure model of interpersonal trust, and proved its rationality by empirical evidence.

The four stages of neighborhood trust: Classic grounded theory.

Neighborhoods are often overlooked as a determinant of health.Among recent research, the focus on "place-based effects," due to prolonged residential environmental exposure, has been of particular interest. These studies' purpose is toidentify and examine how a healthy neighborhood is intentionally created to describe a transferable process-driven theory. A classic grounded theory approach was used in these studies. Data sources include individual in-depth interviews, historical documents, and a member-checking focus group, collected over 3-years. Analysis generated theFour Stages of Neighborhood Trust Model, which is nested within the context of perceived neighborhood safety. The theory outlines a social process of four stages of neighborhood trust: (a) rules-based agreements, (b) shared values, (c) cooperation, and (d) neighborhood belonging. We present the development of a process-driven theory that may be useful for public health nurses as they engage neighborhoods in health promotion activities. The stage of trust development will aid the nurse in identifying what is needed to move to the next stage in a healthy neighborhood process.

A Dynamic Trust Model for Underwater Sensor Networks Fusing Deep Reinforcement Learning and Random Forest Algorithm

Underwater acoustic sensor networks (UASNs) are vital for applications like marine environmental monitoring, disaster prediction, and national defense security. Due to the prolonged exposure of underwater sensor nodes in unattended and potentially hostile environments, the application of UASNs is confronted with numerous security threats. Trust models are an important means to detect anomalous nodes in UASNs and ensure security. However, when confronted with intricate underwater surroundings, the assessment of trust is prone to disruption, and current trust models lack a flexible mechanism for updating trust. Consequently, this study introduces a dynamic evaluation trust model (DRFTM) for underwater acoustic sensor networks that integrate deep reinforcement learning and the random forest algorithm. First, the DRFTM comprehensively considers indicators including communication, data, energy, and environment to provide reliable trust evidence for the next evaluation; second, under the conditions of node mobility and dynamic updating of network topology, we propose a predictive model for assessing the trust status of sensor nodes based on random forest training; last, the utilization of deep reinforcement learning is instrumental in determining the most effective trust update strategy, leading to improved detection accuracy of the trust model. The simulation results demonstrate the effectiveness of the DRFTM in detecting malicious nodes, reducing false positives, and accurately assessing trust, achieving a remarkable 99% accuracy in identifying malicious nodes.

Blockchain-based cyber-security trust model with multi-risk protection scheme for secure data transmission in cloud computing

Blockchain-based cyber-security trust model with multi-risk protection scheme for secure data transmission in cloud computing

Voice-based personal assistant (VPA) trust: Investigating competence and integrity

While intelligence of voice-based personal assistants (VPAs) is evolving, limited research has investigated how various intelligence attributes impact consumer trust and adoption of VPAs. Drawn from social agency theory and the technology acceptance model, the current study developed and empirically tested a holistic research framework of the effects of intelligence attributes on consumers’ behavioral responses to VPAs through trust. We identified three aspects of intelligence attributes as functional (ease of use, usefulness), hedonic (enjoyment, innovativeness), and anthropomorphic attributes (humanness, autonomy) and two types of trust in VPAs as competence and integrity. Results showed dynamic relationships among VPAs’ intelligence attributes, consumer trust, and behavioral intentions. Specifically, both functional and hedonic attributes promote competence, whereas anthropomorphic attributes increase integrity. Further, both competence and integrity lead to positive word-of-mouth (WOM), while only competence positively influences consumers’ usage intention. In addition, a user's level of technology anxiety (an individual difference variable) was found to serve as a moderator in the trust model. This study offers both theoretical contributions and practical implications regarding consumer trust development in the context of VPAs.

Provably secure data selective sharing scheme with cloud-based decentralized trust management systems

AbstractThe smart collection and sharing of data is an important part of cloud-based systems, since huge amounts of data are being created all the time. This feature allows users to distribute data to particular recipients, while also allowing data proprietors to selectively grant access to their data to users. Ensuring data security and privacy is a formidable task when selective data is acquired and exchanged. One potential issue that emerges is the risk that data may be transmitted by cloud servers to unauthorized users or individuals who have no interest in the particular data or user interests. The prior research lacks comprehensive solutions for balancing security, privacy, and usability in secure data selective sharing schemes inside Cloud-Based decentralized trust management systems. Motivating factors for settling this gap contain growing concerns concerning data privacy, the necessity for scalable and interoperable frameworks, and the increasing dependency on cloud services for data storage and sharing, which necessitates robust and user-friendly mechanisms for secure data management. An effective and obviously secure data selective sharing and acquisition mechanism for cloud-based systems is proposed in this work. We specifically start by important a common problematic related to the selective collection and distribution of data in cloud-based systems. To address these issues, this study proposes a Cloud-based Decentralized Trust Management System (DTMS)-connected Efficient, Provably Secure Data Selection Sharing Scheme (EPSDSS). The EPSDSS approach employs attribute-based encryption (ABE) and proxy re-encryption (PRE) to provide fine-grained access control over shared data. A decentralized trust management system provides participant dependability and accountability while mitigating the dangers of centralized trust models. The EPSDSS-PRE paradigm would allow data owners to regulate granular access while allowing users to customize data collection without disclosing their preferences. In our strategy, the EPSDSS recognizes shared data and generates short fingerprints for information that can elude detection before cloud storage. DTMS also computes user trustworthiness and improves user behaviour administration. Our research demonstrates that it’s able to deliver trustworthy and safe data sharing features in cloud-based environments, making it a viable option for enterprises seeking to protect sensitive data while maximizing collaboration and utilization of resources.

What affects the level of local social acceptance of salmon farming in Norway?

The concept of a social licence to operate (SLO) that first emerged related to mining has been developed into several strands of theoretical models and used to study the social acceptance of industries in different contexts. There is an emerging literature on SLO for salmon-farming, but very few quantitative analyses are done to identify and assess factors that affect the level of social acceptance. Models that explain social acceptance levels from people's trust in an industry or company, their confidence in governance, and views on procedural and distributional fairness (trust models) are designed for quantitative analysis, and they have been successful in explaining social acceptance levels for mining. In this paper, we use survey data to test whether the factors in trust models can also explain the level of local social acceptance for salmon farming in Norway. From the structural equation modelling analysis, we conclude that these models at best have limited explanatory power in our case. We then develop an alternative model to analyse social acceptance, where factors of perception and attitudes and respondents' individual characteristics are tested as direct regression paths to influence level of acceptance. This model explains the variation in the data well. The factors that most strongly affect the level of acceptance, and which industry or authorities also can influence, are the perception of to what degree aquaculture is environmentally sustainable, whether the industry acts according to society's expectations, and if the industry is trustworthy. Practical implications for the industry and governance are discussed.

Teaming with a Robot in Mixed Reality: Dynamics of Trust, Self-Efficacy, and Mental Models Affected by Information Richness

This study employs an innovative mixed-reality game to investigate trust, self-efficacy, and collaboration willingness with a mobile robot in relation to information and mental models. Previous research has demonstrated the significant impact of introductory information on technology use and trust, which further evolves over the course of interactions. Expanding on this, we explore new facets of information content through comprehensive measurements in two empirical studies: In study 1, 68 participants watched one of two video tutorials (high or low information richness) before collaborating with a physical robot in a shared virtual 3D environment. Repeated measures served to investigate temporal dynamics. In study 2, 37 participants additionally engaged in qualitative interviews to better understand how their mental models changed with increasing robot experience. Both tutorials were found to influence user trust and self-efficacy over time, with no significant differences between the two variants. Users valued information on how to communicate with the robot, but still relied on trial-and-error approaches. We argue for a moderating effect of information content and richness and highlight the need for carefully designed tutorials to enhance human-robot interaction.

Efficient and Secure Communication in VANETS Using Block Chain

We have made great progress in resolving the crucial problem of location privacy in Vehicular Impromptus Networks (VANETs), an essential component of Intelligent Traffic Systems (ITS), using our innovative block chain-based trust management methodology. VANETs' high inherent mobility has long presented security issues, especially when it comes to using Location-Based Services (LBS). Our creative solution gives cars the ability to request LBS while protecting their personal data with a secure verification process. Vehicle privacy is protected via the creation of anonymous shrouding zones, and vehicle behaviour is controlled and governed by an advanced trust management algorithm. By using block chain technology, the system's data security is strengthened even more, resulting in a strong, impenetrable architecture. Thorough testing has proven the system's resilience against a range of assaults based on trust models, proving its efficacy in protecting vehicle privacy. The outcomes of our simulation demonstrate the practicality of our suggested method in actual VANET circumstances, pointing to the possibility of a new era in safe and private ITC systems.

A sustainable and trusted solution for IoT-based federated learning using feedback behavior

Federated learning is defined as one of the solutions that use distributed clients to train and aggregate the target model without sharing the private information of clients in the network. Hence, federated learning may easily handle and tackle the generated information in a more convenient, feasible and secure manner, while involving the intelligent devices to perform testing and training at the local edges. However, the design of such federated learning solutions faces multiple difficulties due to the cost and complexity of information computation, along with a high energy consumption. Furthermore, the identification of resource-constrained devices which ensures significant power consumption along with privacy and security to the devices makes it critical to attain a sustainable federated learning environment. The aim of this paper is to integrate feedback-based and objective trust models to accurately identify the behavior of communicating devices in the network. The secure transmission of information forwarded by trusted IoT devices is computed by the objective model. In addition, the energy consumption required for measuring the authenticity of a device can be further diminished using a behavior and feedback trusted model. In addition, the past history behavior and feedback received from neighboring devices may also contribute to achieving an efficient and sustainable security mechanism with reduced communication steps and computational delay in the network. The proposed solution is evaluated to analyze the feasibility and performance in terms of energy consumption and resource utilization, while also considering various security metrics for comparison against existing methods. The proposed mechanism out-performed 87% improvement in terms of security metrics in comparison of existing approaches.

Secure malicious node detection in flying ad-hoc networks using enhanced AODV algorithm

In wireless networking, the security of flying ad hoc networks (FANETs) is a major issue, and the use of drones is growing every day. A distributed network is created by a drone network in which nodes can enter and exit the network at any time. Because malicious nodes generate bogus identifiers, FANET is unstable. In this research study, we proposed a threat detection method for detecting malicious nodes in the network. The proposed method is found to be most effective compared to other methods. Malicious nodes fill the network with false information, thereby reducing network performance. The secure ad hoc on-demand distance vector (AODV) that has been suggested algorithm is used for detecting and isolating a malicious node in FANET. In addition, because temporary flying nodes are vulnerable to attacks, trust models based on direct or indirect reliability similar to trusted neighbors have been incorporated to overcome the vulnerability of malicious/selfish harassment. A node belonging to the malicious node class is disconnected from the network and is not used to forward or forward another message. The FANET security performance is measured by throughput, packet loss and routing overhead with the conventional algorithms of AODV (TAODV) and reliable AODV secure AODV power consumption decreased by 16.5%, efficiency increased by 7.4%, and packet delivery rate decreased by 9.1% when compared to the second ranking method. Reduced packet losses and routing expenses by 9.4%. In general, the results demonstrate that, in terms of energy consumption, throughput, delivered packet rate, the number of lost packets, and routing overhead, the proposed secure AODV algorithm performs better than the most recent, cutting-edge algorithms.

Research on Secure Community Opportunity Network Based on Trust Model

With the innovation of wireless communication technology and the surge of data in mobile networks, traditional routing strategies need to be improved. Given the shortcomings of existing opportunistic routing strategies in transmission performance and security, this paper proposes a community opportunistic routing decision-making method based on the trust model. This algorithm calculates the node’s trust value through the node’s historical forwarding behavior and then calculates the node’s trust value based on the trust model. Thresholds and trust attenuation divide dynamic security communities. For message forwarding, nodes in the security community are prioritized as next-hop relay nodes, thus ensuring that message delivery is always in a safe and reliable environment. On this basis, better relay nodes are further selected for message forwarding based on the node centrality, remaining cache space, and remaining energy, effectively improving the message forwarding efficiency. Through node trust value and community cooperation, safe and efficient data transmission is achieved, thereby improving the transmission performance and security of the network. Through comparison of simulation and opportunistic network routing algorithms, compared with traditional methods, this strategy has the highest transmission success rate of 81% with slightly increased routing overhead, and this algorithm has the lowest average transmission delay.

Industrial Wireless Internet Zero Trust Model: Zero Trust Meets Dynamic Federated Learning with Blockchain

Industrial Wireless Internet Zero Trust Model: Zero Trust Meets Dynamic Federated Learning with Blockchain

Implementing Blockchain in the Management of Organ Donation and Transplantation

Abstract: Current organ donation and transplantation systems suffer from vulnerabilities to scams and lack of transparency, hindering trust and efficiency. This proposal introduces a groundbreaking blockchain-based solution that combats both issues. By leveraging the blockchain's security and immutability, the system detects and predicts potential scams through AI analysis of realtime data, notifying authorities immediately. Additionally, it strengthens security and trust by ensuring secure participant identities, controlled access to sensitive information, and automated processes via smart contracts. This not only reduces the risk of illegal activity but also fosters fairness and participation, ultimately transforming the system into a life-saving model of transparency and trust. But that's not all. Blockchain securely stores identities, encrypts sensitive information, and automates tasks with smart contracts, eliminating the human error and shady dealings that plague the current system. This creates a web of trust, empowering donors, recipients, and medical professionals alike. The result? A system that's not just secure, but fair and efficient, saving lives and restoring faith in this vital field. It's time to transplant trust into organ donation, and blockchain is the fertile ground for its growth

Kształtowanie kultury zaufania w szkole z perspektywy dyrektorów. Komunikat z badań jakościowych

This article looks at the mechanisms for building a culture of trust in the school environment, as described by the leaders of selected schools. Trust, like other phenomena of social life, is relational in nature and is a key value that determines the quality of interpersonal relationships. The school is treated as a place for the accumulation of trust resources for the multiplication of social capital. A culture of trust is a dynamic system of values, norms and behaviours that are the source of people’s sense of security and willingness to support others. The text takes the form of a research communication and aims to present ways of understanding, developing and sustaining good relationships at school based on a foundation of trust. The practices of creating a trusting environment have been reconstructed from the statements of headteachers of four schools in and around Warsaw. In this way, a kind of anatomical model (structure) of trust was obtained, consisting of the competence and professionalism of principals and teachers, teachers’ freedom and autonomy, the priority role of cooperation, and organisational transparency. The principals of the schools selected for the study are developing their own practices for building a critical culture of trust.

A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust

Internet applications rely on Secure Socket Layer (SSL)/Transport Security Layer (TSL) certifications to establish secure communication. However, the centralized nature of certificate authorities (CAs) poses a risk, as malicious third parties could exploit the CA to issue fake certificates to malicious web servers, potentially compromising the privacy and integrity of user data. In this paper, we demonstrate how the utilization of decentralized certificate verification with blockchain technology can effectively address and mitigate such attacks. We present a decentralized public key infrastructure (PKI) based on a distributed trust model, e.g., Web of Trust (WoT) and blockchain technologies, to overcome vulnerabilities like single points of failure and to prevent tampering with existing certificates. In addition, our infrastructure establishes a trusted key-ring network that decouples the authentication process from CAs in order to enhance secure certificate issuance and accelerate the revocation process. Furthermore, as a proof of concept, we present the implementation of our proposed system in the Ethereum blockchain, confirming that the proposed framework meets the five identified requirements. Our experimental results demonstrate the effectiveness of our proposed system in practice, albeit with additional overhead compared to conventional PKIs.

Finnish music class studies and extracurricular playing in promoting grit and sisu, the inner fortitude: Reconceptualizing sisu as a predecessor of flow, growth and flourishing

This article investigates musical grit and sisu, inner fortitude, as predecessors of flow, flourishing and personal growth. Grit has been identified as an important factor for success. Our aim was to increase knowledge of the benefits of intensified music education for building grit, resilience and fortitude. Data were collected with mixed methods in 2020 from two schools and 96 children. Surveys were filled out by students in both music and regular classes. Qualitative data about music education experiences were collected through interviews and writing tasks from the music class students and interviews with their teachers. Music classes scored higher than regular classes in perseverance, certain dimensions of flow and musical flourishing. Musical flourishing correlated with playing years and had high correlation with self-efficacy, flow experiences and general well-being. The sequence of facing, but also beating, challenges as a group in musical performances and then feeling flow and flourishing seemed to be beneficial for children’s personal growth. Students in music classes learned not only musical skills, but also resilient and optimistic thinking related to their challenges. The theoretical model of Trust, Hope and Love was revised to display how sisu, the inner fortitude, advances flourishing.

Gen Z Customer Loyalty in Online Shopping: An Integrated Model of Trust, Website Design, and Security

The study aimed to examine the direct impact of website design and security on trust and loyalty, with trust serving as a mediator. The research focused on Generation Z consumers purchasing e-commerce products in Indonesia, with 307 respondents. Data collection utilized a Google Form questionnaire survey distributed via social media. Structural equation modeling with AMOS 21 was employed for data analysis. Results indicated positive impacts on loyalty and trust in Generation Z consumers stem from good website design and robust security. Trust was found to positively mediate the relationship between exogenous and endogenous variables. The findings suggest that e-commerce companies in Indonesia can enhance loyalty and trust by prioritizing website design, and security, and investing in strategies that build consumer trust through online marketing and security measures.