Trust Assessment Research Articles

Leveraging Tropical Algebra to Assess Trustworthy AI

Given the complexity of the application domain, the qualitative and quantifiable nature of the concepts involved, the wide heterogeneity and granularity of trustworthy attributes, and in some cases the non-comparability of the latter, assessing the trustworthiness of AI-based systems is a challenging process. In order to overcome these challenges, the Confiance.ai program proposes an innovative solution based on a Multi-Criteria Decision Aiding (MCDA) methodology. This approach involves several stages: framing trustworthiness as a set of well-defined attributes, exploring attributes to determine related Key Performance Indicators (KPI) or metrics, selecting evaluation protocols, and defining a method to aggregate multiple criteria to estimate an overall assessment of trust. This approach is illustrated by applying the RUM methodology (Robustness, Uncertainty, Monitoring) to ML context, while the focus on aggregation methods are based on Tropical Algebra.

Multi Attribute Trust Aware Routing for Adversary Detection in Wireless Sensor Networks

Security attacks has become major obstacles in Wireless Sensor Networks (WSN) and Trust Aware Routing is second line of defense. With an aim to improve on the existing routing mechanisms, in this paper, we propose Interactive, Onlooker and Capability Trust Aware Routing (IOC-TAR), a multi-trust attribute framework for trust management in WSNs. IOC-TAR employs three trust features to establish a trustworthy relationship between sensor nodes for their cooperation. Interactive trust uses communication interactions, onlooker trust uses neighbor node’s opinions and capability trust uses stability and fault tolerance for trust assessment. For, each node, one composite trust factor is formulated and decides its trustworthiness. Extensive simulation experiments are conducted to evaluate the effectiveness and efficiency of proposed IOC-TAR in the identification of malicious nodes and the provision of attack resilience. The results declare that the IOC-TAR enhances the attack resilience by improving Malicious Detection rate and reducing False Positive Rate.

Trust Assessment Skills Among Close Friends in a Ubiquitous Social Media Environment

For ages, individuals have relied on trust assessment skills to evaluate spoken words and accompanying body language social signals (BLSS) in friendships. This interactive process seeks to avoid harmful associations and unreliable information. However, with widespread social media use, assessment of BLSS is difficult, and users commonly report harm from defective estimations of interactions. This study evaluates how individuals adjust their trust assessment skills for close social media friendships, given the importance they place on BLSS in its role as a moderator. The research utilizes PLS-SEM, ANOVA, and cluster analysis to evaluate 247 U.S. online survey participants aged 18 to 67. The study adapts Mayer et al.’s trust model using social learning and cognitive dissonance theories. The independent variables, ability, benevolence, and integrity, strongly predict the dependent trust belief. In line with cognitive dissonance theory, BLSS has a negative and significant moderating effect on ability. BLSS reflects a small effect and widely insignificant moderating relationship with benevolence; analysis shows that a moderating relationship should not apply to benevolence. Counter to the hypothesized direction, BLSS produced a positive moderating effect on integrity, which was insignificant by a narrow margin. The analysis explains the basis for the positive relationship. Lastly, statistical analysis reveals an age-based shift in the relevancy of BLSS; younger users place less importance on BLSS, whereas older users place more importance on BLSS. This position for younger users aligns with their exposure and acclimation to social environments with widespread mobile communication and social media use since childhood.

The level of Emotional Value and Customer Trust Assessment of State-Owned Enterprise Bank Customer of Kebayoran Baru South Jakarta

Purpose: The rapid development of economy in Indonesia has given a positive influence to the services sectors including State-Owned Enterprise banking, everyone will need banks as their business partners. Customers’ trust is a great asset for banks. The long-term relationship of customer-banks depends on the presence of customers’ trust on the banks. The significance of this phenomenon in banking industry becomes more critical where banks have to know their individual customers as well as contextual factors that influence on customers’ emotional value and customers trust.Design/methodology: This study aims to measure the level of customers emotional value and customers’ trust. 400 customers from any state-owned enterprise bank in Kebayoran Baru - South Jakarta were selected to form the sampling frame.Findings: The results show that both emotional value and customer trust is at a good level. All products, services and product features are able to create the emotional value expected by customers. All employees, products and facilities provided by bank are able to create consumers trust in the bank.Practical implications: The empirical implication of this research is to determine the conditions and the level of emotional value and customer trust in state-owned enterprise bank.Originality/Value: All companies in the banking industry must increase their awareness of the importance of providing value related to the emotional side of customers and also the importance of maintaining customer trust. The contribution of this research focuses on efforts to attract customer through their emotional value and trust.Keywors: Emotional Value, Customer Trust, State-Owned Enterprise Bank.

Adaptive multi-granularity trust management scheme for UAV visual sensor security under adversarial attacks

The big data provided by unmanned aerial vehicle (UAV) visual sensors offers essential information resources for activities across various industries. However, various adversarial threats are inevitable throughout the lifecycle of data generation, transmission, and utilization, leading to serious security risks. Trust assessment of visual sensors is a prerequisite for securing UAVs, but the multidimensionality of the trust elements and the uncertainty of the evidence limit its practical application. To advance this research, we innovatively propose a trust management scheme based on multi-granularity evidence fusion within the framework of belief functions (BFs) theory to adaptively respond to both known and unknown threats. We first propose a direct trust assessment model for known threats, which constructs multidimensional coarse-grained trust elements (MCTEs) and integrates multiple lightweight sub-models for basic belief assignment (BBA) to meet the need for fast response. Then, to address the unknown threats, we introduce pre-trained models to build multidimensional fine-grained trust elements (MFTEs) to construct trust recommendation models for indirect trust assessment for visual sensors. In addition, to accurately characterize the trustworthiness of visual sensors, we also introduce a BBA-weighted fusion method to achieve more reasonable trust aggregation by weakening highly conflicting evidence sources. Finally, to validate the effectiveness of the proposed method, we conducted a comprehensive trust assessment and security experiment on UAV aerial images. The results indicate that the proposed method demonstrates excellent performance and is beneficial for enhancing UAV security in adversarial attack scenarios.

Ensuring Information Security of Information Systems to be Integrated Based on Trust

Relevance. Information systems are integrated with each other, which leads to the need to ensure the protection of the integrated system. The level of trust requires formalizing the concept of trust and studying its nature and structure.The purpose of the article is to remove the contradiction between the needs to provide access to the resources of the resulting integrated system and ensure compliance with the information security requirements of each of the integrated systems by formulating the concept of trust from the information security perspective. Methods used: systems analysis, risk management theory, resolutions, iSoft operator equation synthesis method.Results. Main shortcomings of existing approaches to the formalization of the concept of “trust” are identified. Based on the FIST information system model, a functional trust structure has been developed and formalized in IDEF0 notation for all levels of integrated information systems: supporting level, personnel level, hardware and software levels. Examples of violation of trust and examples of tools for creating trust for each level of the information system are given. The adequacy of the model is illustrated by the example of real integration of information systems. Application of the proposed trust model made it possible to identify features that increase information security risks for the integrated information system from the example. Novelty. An interpretation of trust as a measure of information security is proposed, in contrast to “risk” as a measure of danger. A tool for quantitative assessment of trust is proposed. A necessary and sufficient condition for creating maximum trust in an information system is formulated and proven by the resolution method Practical significance. The proposed trust model can be used in the development of guidance documents regulating the process of integration of information systems, in setting requirements for service personnel and creating training programs for them, for developing information security tools and methods for their application.

Behave Yourself! Behavioral Indicators of Trust in Human-Agent Teams

As advancements in artificial intelligence accelerate, there is a rise in the complexity and number of autonomous agents placed in human-agent teams (HATs). With this expansion, it is important to understand how trust in agent teammates evolves and is influenced by contextual events. In support of this, significant research has focused on the factors that influence human trust in an agent and elements that negatively impact this trust. In this research, human trust in agent teammates is typically measured via self-report surveys. Although a reliable format, surveys are not without limitations, as they can be disruptive and lack the temporal resolution needed to capture nuanced and dynamic changes in trust. As a result, researchers are adopting alternative measurement approaches that aim to unobtrusively capture measures that correlate with trust using behavioral and physiological sensors that capture measures such as voice pitch and eye tracking. However, most of these studies utilize only one unobtrusive measure, which can be indicative of various states (e.g., heart rate varies with psychological and physical responses). Given the lack of selectivity in such measures, it may be necessary to utilize multiple unobtrusive measures at once, such as real-time recorded behavioral indicators, to effectively capture changes in these indicators that are due to changes in trust over time. This paper investigated the following research questions: (1) do multiple unobtrusive indicators provide a more holistic picture of trust compared to a single behavioral indicator, and (2) are certain behavioral indicators more indicative of self-reported trust at different times during a mission (e.g., before/after a trust violation event)? Results indicated that following a significant event, changes in self-reported trust are associated with specific behaviors related to improving or mitigating perceived deficiencies in their agent teammates. These insights highlight the opportunity to integrate dynamic behavioral measures in trust assessment frameworks, specifically in scenarios where a significant event compromises trust.

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.

Integrating focal vulnerability into trust research

ABSTRACT Trust – most often defined as the trustor’s willingness to accept vulnerability – matters across almost every facet of modern life. Significant bodies of research have been devoted to understanding the nature and dynamics of the construct, but much less work has directly considered the vulnerability at the core of its definition. The current article addresses this gap, elucidating the concept of focal vulnerability as the portion of the trustor’s individualised, total potential for experiencing harm that is perceived, relational, and contemporaneous with their assessment of trust. We then position this concept as the vulnerability that a trustor is, by definition, willing to accept. Following work in other literatures, we consider the dynamics of focal vulnerability by exploring the systematic ways in which it is likely to vary. We then argue that this variability can be meaningfully captured by assessing the extent to which the trustor experiences felt vulnerability, an aversive recognition of their potential for experiencing harm. We conclude by exploring the implications of this argument and outline several untested assumptions that are ripe for future research.

A Consumer Trust Assessment Model for Online Shopping Based on Fuzzy Fusion Decision-Making

With the rapid development of e-commerce, online shopping has become an indispensable part of people's daily lives. However, consumers often face trust issues during online shopping, such as product quality and seller integrity, which directly impact their shopping experience and purchasing decisions. Therefore, accurately assessing consumer trust has become a crucial task. This study first constructs a consumer trust assessment system, analyzing and selecting key factors related to consumer trust, and establishes a model for assessing consumer trust for online shopping. Subsequently, we propose an assessment method based on text mining and deep learning sentiment analysis techniques to extract consumer sentiment information from specified consumer reviews. Furthermore, through fuzzy decision-making fusion strategy, we integrate sentiment information from the dimensions of quality assurance, reliability, and responsiveness to enhance the accuracy of the assessment.

QS-Trust: An IoT ecosystem security model incorporating quality of service and social factors for trust assessment

In the rapidly growing and increasingly complex Internet of Things (IoT) ecosystem, securing communication and data exchanges between devices is a major concern. To address this, we proposed QS-Trust, a trust-based security model considering both Quality of Service (QoS) and social parameters. QS-Trust uses a trust value to determine the trust level between devices and employs a QoS-aware trust-based algorithm to improve the security of data transmissions. Additionally, the model incorporates intelligence parameters such as computing power, memory capacity, device behavior and context information to enhance the accuracy of trust evaluation. Our simulation results demonstrated that QS-Trust effectively improved the security of the IoT ecosystem while maintaining the high level of QoS. The execution time of QS-Trust was in the range of 21 to 128 milliseconds, which is efficient for real-time IoT applications. QS-Trust offers a promising solution for securing the IoT ecosystem. The QS-Trust model effectively addresses the challenges of maintaining accurate and up-to-date trust levels in dynamic IoT environments through its decentralized approach, multi-factor evaluations, and adaptive algorithms. By continuously monitoring device performance and interactions and dynamically adjusting trust scores, QS-Trust ensures that the IoT network remains secure and reliable.

EVALUATION OF INTERPERSONAL TRUST AND GROUP AFFILIATION AMONG CHILDREN WITHOUT PARENTAL CARE IN RELATION TO AGE

The aim of the study was to determine the difference in interpersonal trust and group affiliation among children without parental care in relation to age. The study included a convenience sample of 122 participants of both genders, who were accommodated inthe Children's SOS Village Gračanica (Dječije SOS selo Gračanica), Village of Peace Turija (Selo mira Turija), and the Home for Children without Parental Care in Tuzla (Dom za djecu bez roditeljskog staranja u Tuzli). In order to verify the set research goal, the Group Affiliation Scale and the Interpersonal Trust Scale were utilized. Univariate analysis of variance was applied to determine the difference in interpersonal trust and group affiliation among children without parental care in relation to age. The obtained data were processed using the statistical package SPSS 20 for Windows. Based on the results, it can be concluded that there is a statistically significant difference in the assessment of interpersonal trust and group affiliation among childrenwithout parental care in relation to age.Keywords: children without parental care, children’s age, interpersonal trust, group affiliation.

LTEM: Lightweight Trust Evaluation Model in IoT Environment

In the domain of the Internet of Things (IoT), data reliability is important, particularly within critical sectors like healthcare, environmental surveillance, and smart grids. Nevertheless, data transfer from the physical domain to the digital layer is susceptible to trust-related challenges encompassing data integrity, genuineness, and credibility. Predominantly, prevailing models for trust assessment primarily concentrate on the conduct of nodes, thus disregarding the direct evaluation of data packets. This particular constraint results in an insufficient validation of data credibility, thereby failing to consider pivotal elements like timeliness and accuracy. Furthermore, utilizing cloud-based packet evaluation frameworks frequently leads to inaccuracies, unreliability, and energy inefficiencies owing to the transfer of untrusted data. The current study introduces a streamlined trust assessment framework called the lightweight trust evaluation model (LTEM), custom-built for IoT settings to combat these obstacles. LTEM meticulously examines node behavior and data packets via a multi-tiered approach encompassing nodes, cluster heads (CH), and base stations (BS). Moreover, the proposed model's architecture considers energy usage by averting the transmission of untrusted data. Simulation results showcase the supremacy of LTEM compared to existing models by achieving a detection rate of 99% for untrusted data packets, outperforming the detection rates ranging from 30% to 75% observed in other models. Moreover, LTEM enhances the operational efficiency of sensor nodes regarding energy consumption, achieving an average energy utilization of 1.33J out of 4J, resulting in savings of approximately 2.67J on average, thereby extending the lifespan of nodes.

Segmented Trust Assessment in Autonomous Vehicles via Eye-Tracking

Segmented Trust Assessment in Autonomous Vehicles via Eye-Tracking

EmoConnect: Nurturing Trust and Relationship Bonds in Alzheimer’s Conversations

Alzheimer's disease presents a global challenge, affecting the ability of individuals to communicate and trust their caregivers. Effective caregiver interactions are vital for emotional well-being in Alzheimer's patients. However, the progressive nature of the disease often leads to communication barriers and a lack of trust, exacerbated by the absence of objective trust assessment mechanisms. Additionally, patients are unable to recall past conversations. To address these challenges, EmoConnect proposes an innovative ML- based solution. EmoConnect solution involves the development of an ML based application that employs brain wave detection and analysis to objectively assess caregiver trustworthiness during interactions with Alzheimer's patients. It calculates a trustworthiness score, enhancing the quality of caregiving and promoting emotional well- being for both caregivers and patients. Furthermore, it stores the text of conversations alongside trust scores, enabling patients to access and review past interactions.

Balancing Human Likeness in Social Robots: Impact on Children’s Lexical Alignment and Self-disclosure for Trust Assessment

While there is evidence that human-like characteristics in robots could benefit child-robot interaction in many ways, open questions remain about the appropriate degree of human likeness that should be implemented in robots to avoid adverse effects on acceptance and trust. This study investigates how human likeness, appearance and behavior, influence children’s social and competency trust in a robot. We first designed two versions of the Furhat robot with visual and auditory human-like and machine-like cues validated in two online studies. Secondly, we created verbal behaviors where human likeness was manipulated as responsiveness regarding the robot’s lexical matching. Then, 52 children (7-10 years old) played a storytelling game in a between-subjects experimental design. Results show that the conditions did not affect subjective trust measures. However, objective measures showed that human likeness affects trust differently. While low human-like appearance enhanced social trust, high human-like behavior improved children’s acceptance of the robot’s task-related suggestions. This work provides empirical evidence on manipulating facial features and behavior to control human likeness in a robot with a highly human-like morphology. We discuss the implications and importance of balancing human likeness in robot design and its impacts on task performance, as it directly impacts trust-building with children.

What drives trust in regulatory agencies? Probing the relevance of governmental level and performance through a cross-national elite experiment on EU regulation

Abstract Trust between constituent actors within the European Union (EU)’s multilevel regulatory regimes is decisive for regulatory success. Trust drives information flows, increases compliance, and improves cooperation within these regimes. Despite its importance, systematic knowledge regarding the drivers of trust within regulatory regimes is limited. This paper inquires whether trust in regulatory agencies is influenced by their affiliation with the national or EU governmental level, as well as by their performance. While existing literature predominantly focuses on why citizens place their trust in governments or regulatory agencies, this paper presents original insights regarding the formation of trust among elites within the regulatory regime, including politicians, ministerial officials, agency officials, interest groups, and regulated entities. We employ data obtained from a large-scale vignette experiment conducted in six countries involving 752 decision-makers from relevant organizations. The experimental results suggest that both public and private elite actors’ trust assessment of regulatory agencies does not hinge on cues associated with the governmental level, but rather depends on agency performance. Accordingly, belonging to the national or EU governmental level does not create a difference in trust assessment of regulatory agencies in itself. It, however, shows that particularly elite actors are rather sensitive in terms of the performance of a regulatory agency.

Taas: Trust assessment as a service for secure communication of green edge-assisted UAV network

Taas: Trust assessment as a service for secure communication of green edge-assisted UAV network

Оценка индивидуально-психологических характеристик и уровня доверия по фотоизображению лица незнакомого человека до и после непосредственного перцептивного взаимодействия

<p>The article presents the results of a study of the influence of direct perceptual interaction on the assessment of the individual psychological characteristics of a stranger and the level of trust/distrust in him. The problem of the influence of direct perceptual interaction in conditions of interpersonal perception still remains relevant and requires thorough experimental study. The main assumption of the work is that ideas about a stranger, including a complex dialectical interaction of trust/distrust, will differ significantly before and after direct perceptual interaction. The assessment of individual psychological characteristics was carried out using the “Personal Differential” technique, trust/distrust using the “Perceptive Trust Assessment Test”. The results show that the experience of brief nonverbal face-to-face interaction reduces the number of significant differences in the ratings of naive observers with the ratings of experts. Regardless of the facial morphotype, after the experience of interaction, all sitters are perceived by observers with a bias towards socially desirable individual psychological characteristics, with the exception of the only characteristic (stubborn) in the assessments of the round morphotype. The experience of non-verbal face-to-face interaction has a significant impact on the formation of a more trusting attitude towards a stranger in an outside observer, in contrast to the level of trust formed from a photograph.</p>

Hybrid Secure Cluster-Based Routing Algorithm for Enhanced Security and Efficiency in Mobile Ad Hoc Networks

ABSTRACT This research addresses critical gaps in Mobile Ad hoc Networks (MANETs) by proposing a hybrid secure cluster-based routing algorithm, focusing on enhancing network security, robustness, and reliability through multipath routing. Methodologically, the approach integrates Convolutional Neural Networks (CNN) for optimal path routing and Emperor Penguin Optimization (EPO) for clustering, introducing a novel combination for efficient cluster head selection. A novel contribution lies in the development of a prediction technique utilizing a trust assessment algorithm to calculate direct trust ratings at each node, incorporating fuzzy values between zero and one. Trust values are further influenced by node performance, adding a dynamic dimension to the trust evaluation process. Key novelties include the emphasis on energy efficiency, network longevity, remaining energy, security level, bandwidth, and packet delivery ratio as evaluation criteria. The proposed CNN-EPO model demonstrates superior results compared to traditional routing protocols, achieving a remarkable 95% energy efficiency, a heightened security level of 99%, and a throughput reaching up to 8 Mbps. Additionally, the Packet Delivery Ratio (PDR) attains close to 99% and routing overhead remains below 0.5, ensuring efficiency in challenging network scenarios with 50 adversaries. In summary, this research contributes a comprehensive solution to MANET challenges, introducing a novel hybrid routing algorithm, incorporating advanced methodologies for path optimization and clustering. These outcomes highlight how important the suggested strategy is to improve the existing state of the art in MANETs.