Trust Evaluation Research Articles

A bird's-eye view of phubbing: How adult observations of phone use impact judgments, epistemic trust, and interpersonal trust

Research demonstrates that phubbing—the act of snubbing someone in a social setting by looking at one's mobile phone—interferes with the quality and satisfaction of social interactions. This article examined how observations of an adult's phone use during a social interaction impact different social judgments. Adult participants (n = 331) watched a 3-min video showing four speakers having a discussion around a table. One speaker looked at their phone intermittently during the meeting (five times for 2–3 s each) while not interacting with the other speakers. Participants were asked to rate characteristics (e.g., attentiveness) of all four speakers and evaluate who they could trust both epistemically (e.g., for knowledge) and interpersonally (e.g., for social connection). Participants also provided information about their own technology use. Results indicated that participants’ judgments, ratings of interpersonal trust, and epistemic trust toward the phone user were significantly lower when compared to their ratings toward the non-phone users in the video, especially among female participants. Additionally, the more participants reported using their own phones during group interactions, the more leniently they rated the phone user in the video. This research provides evidence that overseeing acts of co-present mobile phone use are negatively evaluated. This has implications for how adults view technology use during social engagements.

A survey of the state‐of‐the‐art approaches for evaluating trust in software ecosystems

AbstractThird‐party software has streamlined the software engineering process, allowed software engineers to focus on developing more advanced components, and reduced time and cost. This shift has led to software development strategies moving from competition to collaboration, resulting in the concept of software ecosystems, in which internal and external actors work together on shared platforms and place their trust in the ecosystem. However, the increase in shared components has also created challenges, especially in security, as the large dependency trees significantly enlarge a system's attack surface. The situation is made worse by the lack of effective ways to measure and ensure the trustworthiness of these components. In this article, we explore current approaches used to evaluate trust in software ecosystems, focusing on analyzing the specific techniques utilized, the primary factors in trust evaluation, the diverse formats for result presentation, as well as the software ecosystem entities considered in the approaches. Our goal is to provide the status of current trust evaluation approaches, including their limitations. We identify key challenges, including the limited coverage of software ecosystem entities; the objectivity, universality, and environmental impacts of the evaluation approaches; the risk assessment for the evaluation approaches; and the security attacks posed by trust evaluation in these approaches.

Trusting information from friends: Adults expect it but preschoolers do not.

As adults, we might understand that beliefs often spread because people are strongly influenced by their friends, family, and other social connections. However, do we think those influences are strong enough to overrule direct evidence of a friend's unreliability? And do preschoolers expect people to show such biases toward friends and to privilege friendship over reliability? Across three experiments, we explored whether friendship influences the evaluations of trust when others learn labels for novel objects as well as personal opinions. After watching scenes involving a main character, her best friend, and a stranger, preschoolers and adults judged who would be trusted for information from the main character's perspective (third person) as well as their own (first person). Adults (n = 128, 55 female, recruited online from across the United States) expected the main character to trust information from her friend even if she had been previously inaccurate, while basing their own first-person judgments on accuracy. In contrast, 4- and 5-year-olds (n = 128, 62 female, from the United States) thought that the main character would be like themselves and prioritize accuracy over friendship. Further, preschoolers expected the main character to trust her (inaccurate) friend and (accurate) stranger equally when forming personal opinions. Thus, young children, unlike adults, do not expect others' epistemic trust to privilege friendship with the speaker over accuracy information. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

VIRTSI: A novel trust dynamics model enhancing Artificial Intelligence collaboration with human users – Insights from a ChatGPT evaluation study

The rapid integration of intelligent processes and methods into information systems in the Artificial Intelligence (AI) era has led to a substantial shift towards autonomous software decision-making. This evolution necessitates robust human oversight, especially in critical domains like Healthcare, Education, and Energy. Human trust in AI plays a vital role in influencing decision-making processes of users interacting with AI.This paper presents VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), a novel rigorous computational model for human-AI Interaction. VIRTSI simulates human trust states, spanning from overtrust to distrust, through user modelling. It comprises: 1. A trust dynamics representational model based on Deterministic Finite State Automata (DFAs), illustrating transitions among cognitive trust states in response to AI-generated replies. 2. A trust evaluation model based on Confusion Matrices, originating from machine learning and Accuracy Metrics, providing a quantitative framework for analysing human trust dynamics. As a result, this is the first time that trust dynamics have been thoroughly traced in a representational model and a method has been developed to assess the impact of possibly harmful states like overtrust and distrust.An empirical study on the recently launched Large Language Model of generative AI, ChatGPT (version 3.5), provides a radical underexplored AI-generated platform for evaluating the human-AI interaction through VIRTSI. The study involved 1200 interactions of real users as well as AI experts together with experts in two very different domains of evaluation, namely software engineering and poetry. This study traces trust dynamics and the emerging human-AI interaction, in concrete examples of real user synergies with generative AI. The research reveals the vital role of maintaining normal trust states for optimal human-AI interaction and that both AI and human users need further steps towards this goal. The real-world implications of this research can guide the creation and evaluation of user interfaces with AI and the incorporation of functionalities in the development of generative AI chatbots in terms of trust by providing a new rigorous DFA representational method of trust dynamics and a corresponding new perspective of confusion matrix evaluation method of the dynamics’ impact in the efficiency of human-AI dialogues.

TwI-FTM: Two-way IoT-FoG trust management scheme for task offloading in IoT-FoG networks

Based on trust management, the service establishment between IoT/User devices and fog networks reduces the impact of malicious data and nodes. Hence, building a trusted environment in the IoT and fog layers is very important. There is no assurance that the generated data or task is standard data; it may also have abnormal data to modify its originality. Similarly, the nodes in the fog environment also have malicious nodes during task offloading. For this, the novel Two-way IoT data & Fog Trust management (TwI-FTM) algorithm is proposed to identify the abnormal data with the help of the Trusted Data Measurement (TDM) and Fog Access Trust Evaluation (FATE) method for identifying the malicious fog access nodes in fog network. Identifying these abnormal data and malicious fog access nodes is done by continuously monitoring fog control nodes with the threshold level. Once it is identified, they will automatically be removed from the IoT-FoG network. The obtained trust measurements are analyzed and compared with other existing methods; it is identified that the proposed TwI-FTM outperforms and has a higher trust degree of 0.68 compared with others. It achieved higher detection accuracy of malicious fog access nodes in fog networks at 21 %, 16 %, and 14 % compared with OTM, TTM, and SLA-Trust, respectively. Finally, the task completion time was also reduced, and it was achieved at 4.5 ms, 5 ms, and 13 ms compared with TTM, OTM, and SLA-Trust, respectively.

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.

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.

Pairing in-vehicle intelligent agents with different levels of automation: implications from driver attitudes, cognition, and behaviors in automated vehicles

ABSTRACT In-vehicle intelligent agents (IVIAs) have been developed to improve user experience in autonomous vehicles. Yet, the impact of the automation system on driver behavior and perception toward IVIAs is unclear. In this study, we conducted three experiments with 73 participants in a driving simulator to examine how automation system parameters (the level of automation system and IVIA features) influence driver attitudes, cognition, and behaviors when driving or riding in a simulated vehicle. We focused on subjective evaluations of driver-agent interaction and driver trust toward IVIAs to assess driver attitudes, driver situation awareness, and visual distraction to capture their cognition, and their driving performance to understand their behaviors. Our results show that the level of automation system affects drivers’ attitudes toward agent capabilities (e.g. perceived intelligence). Embodiment benefits are more pronounced with Level 5 systems, while speech style, in general, is more influential in determining affective aspects of user attitudes (e.g. Warmth, Likability). As the level of automation increases, drivers engage in more visual distractions. In addition, conversational speech style in general encouraged safer driving behaviors indicated by more stable lateral control under lower levels of automation. Our findings uncover the path of how system parameters affect driver behaviors through system evaluation and trust in agents. These findings have important implications for the development of cohesive user experiences in future transportation systems.

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.

Interactive effects of users’ openness and robot reliability on trust: evidence from psychological intentions, task performance, visual behaviours, and cerebral activations

Although trust plays a vital role in human-robot interaction, there is currently a dearth of literature examining the effect of users’ openness personality on trust in actual interaction. This study aims to investigate the interaction effects of users’ openness and robot reliability on trust. We designed a voice-based walking task and collected subjective trust ratings, task metrics, eye-tracking data, and fNIRS signals from users with different openness to unravel the psychological intentions, task performance, visual behaviours, and cerebral activations underlying trust. The results showed significant interaction effects. Users with low openness exhibited lower subjective trust, more fixations, and higher activation of rTPJ in the highly reliable condition than those with high openness. The results suggested that users with low openness might be more cautious and suspicious about the highly reliable robot and allocate more visual attention and neural processing to monitor and infer robot status than users with high openness.

Comparing preferences for skin cancer screening: AI-enabled app vs dermatologist

Background and aimSkin cancer is a major public health issue. While self-examinations and professional screenings are recommended, they are rarely performed. Mobile health (mHealth) apps utilising artificial intelligence (AI) for skin cancer screening offer a potential solution to aid self-examinations; however, their uptake is low. Therefore, the aim of this research was to examine provider and user characteristics influencing people's decisions to seek skin cancer screening performed by a mHealth app or a dermatologist. MethodsTwo forced-choice conjoint experiments with Nmain = 1591 and Nreplication = 308 participants from the United States were conducted online to investigate preferences for screening providers. In addition to the provider type (mHealth app vs dermatologist), the following provider attributes were manipulated: costs, expertise, privacy policy, and result details. Subsequently, a questionnaire assessed various user characteristics, including demographics, attitudes toward AI technology and medical mistrust. ResultsOutcomes were consistent across the two studies. The provider type was the most influential factor, with the dermatologist being selected more often than the mHealth app. Cost, expertise, and privacy policy also significantly impacted decisions. Demographic subgroup analyses showed rather consistent preference trends across various age, gender, and ethnicity groups. Individuals with greater medical mistrust were more inclined to choose the mHealth app. Trust, accuracy, and quality ratings were higher for the dermatologist, whether selected or not. ConclusionOur results offer valuable insights for technology developers, healthcare providers, and policymakers, contributing to unlocking the potential of skin cancer screening apps in bridging healthcare gaps in underserved communities.

IoT-based external attacks aware secure healthcare framework using blockchain and SB-RNN-NVS-FU techniques.

In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network nodes in IoT healthcare are vulnerable to an increased level of security threats. This paper introduces a secure Electronic Health Record (EHR) framework with a focus on IoT. Initially, the IoT sensor nodes are designated as registered patients and undergo initialization. Subsequently, a trust evaluation is conducted, and the clustering of trusted nodes is achieved through the application of Tasmanian Devil Optimization (STD-TDO) utilizing the Student's T-Distribution. Utilizing the Transposition Cipher-Squared random number generator-based-Elliptic Curve Cryptography (TCS-ECC), the clustered nodes encrypt four types of sensed patient data. The resulting encrypted data undergoes hashing and is subsequently added to the blockchain. This configuration functions as a network, actively monitored to detect any external attacks. To accomplish this, a feature reputation score is calculated for the network's features. This score is then input into the Swish Beta activated-Recurrent Neural Network (SB-RNN) model to classify potential attacks. The latest transactions on the blockchain are scrutinized using the Neutrosophic Vague Set Fuzzy (NVS-Fu) algorithm to identify any double-spending attacks on non-compromised nodes. Finally, genuine nodes are granted permission to decrypt medical records. In the experimental analysis, the performance of the proposed methods was compared to existing models. The results demonstrated that the suggested approach significantly increased the security level to 98%, reduced attack detection time to 1300 ms, and maximized accuracy to 98%. Furthermore, a comprehensive comparative analysis affirmed the reliability of the proposed model across all metrics. The proposed healthcare framework's efficiency is proved by the experimental evaluation.

Qualitative user experience evaluation of the MS trust's online treatment decision aid tool's accommodation of planning pregnancy.

Decision-making about treatment when planning a pregnancy (family planning) is complex for women living with multiple sclerosis (MS). Decision tools can help this process, in 2016 MS Trust launched their online digital treatment decision tool to support people with MS. To evaluate user-experience of this tool by exploring women's opinions about its content, interface, and usefulness in the context of family planning; and to synthesize recommendations to improve the tool. Thirty participants qualitatively evaluated the tool using Think Aloud methodology. Sessions were conducted online using Microsoft Teams and were video recorded. Transcription was automated and data were thematically analyzed. Women's first impression was that the tool presented a lot of information at once, which was difficult to take in, and they found it difficult to navigate. Although the tool was helpful in allowing them to compare treatment options, the filters were confusing, and the information related to pregnancy sometimes contradicted advice from their healthcare practitioners. They suggested rewording the pregnancy recommendations and filters, updating some content, and making some changes to the interface to meet users' cognitive needs. The MS Trust treatment decision tool is excellent in helping women with treatment choices at initial diagnosis. However, it is not currently as useful when considering family plans. Recommendations were conveyed to MS Trust where some are now applied to the new live version and the rest are to be considered for future updating projects.

RTE: Rapid and Reliable Trust Evaluation for Collaborator Selection and Time-Sensitive Task Handling in Internet of Vehicles

RTE: Rapid and Reliable Trust Evaluation for Collaborator Selection and Time-Sensitive Task Handling in Internet of Vehicles

ORGANIZATION OF WORK OF THE "PROZORRO" SYSTEM AS A MECHANISM OF ECONOMIC SECURITY AND PROTECTION OF INVESTMENT RESOURCES AIMED AT THE RESTORATION AND DEVELOPMENT OF STATE INSTITUTIONS AND UTILITY ENTERPRISES

Annotation. In the context of war and post-war reconstruction, the issue of transparent utilization of investment resources becomes critically important for maintaining the trust rating of foreign investors and directly impacts the economic well-being and security of enterprises. The research aims to elucidate theoretical and practical aspects concerning the directions for the development and modernization of protection against corruption risks in tender and public procurement systems. Employing dialectical, historical, and systemic methods of cognition, the analytical systematization of previous scientific studies is conducted. Results. The study presents a systematic analysis of prior research on the effectiveness of the "PROZORRO" system amidst procurement abuses. The research process is based on methods of structural-logical analysis for constructing the logic and systematization of key aspects of operation; a systemic approach, analysis, and synthesis to develop clear algorithms for the modernization of the "PROZORRO" system. The scientific novelty of the study lies in the formulation of contemporary methods to enhance the efficiency of the "PROZORRO" system. Practical significance lies in the development of an Information-analytical concept for improving the "PROZORRO" system, including enhancing the software interface and processing statistical data of the "PROZORRO" system.

Trust evaluation model immune to decoherent noise in quantum communication network

Trust evaluation model immune to decoherent noise in quantum communication network

Energy aware and secure routing for hierarchical cluster through trust evaluation

With the rapid growth of technology advancement in wireless communication have resulted in interconnection of large number of devices covering wide range of application services. Wireless sensor network (WSN) have become popular and provide various benefits, including low-power smart computation, compact size, self organizing and ability to route via routing protocols. WSN covers a wide range of application services, including home automation, smart cities, fire detection, unmanned aerial vehicle (UAV), and object tacking. Sensor nodes are tiny, battery operated, and suffer from fundamental restrictions such as available bandwidth, memory space, power restrictions, and limited resources. Due to their communication characteristics and working environment, WSN faces energy optimization and secure data communication challenges to increase network lifetime and defend against malicious nodes. To overcome energy scarcity and consumption issues, clustering methods have proven smart and effective approaches to reduce energy consumption and extend network life. Low energy adaptive clustering hierarchy (LEACH), well-known clustering method, enhances energy efficiency by selecting cluster head (CH) on a rotation basis by employing random integer technique. To extend network stability period, extensions of LEACH was proposed. LEACH and its extensions mainly focus on optimal selection of CH and fail to concentrate on secure communication. Integration of blockchain and real-time message content validation (RMVC) in LEACH was modelled to detect malicious node through secure hash algorithm. However, existing scheme had complex cryptographic functions and higher computation overhead. In this paper, we present energy aware and secure routing (EASR) for hierarchical cluster based WSN. Existing LEACH is modified and an improved threshold value is adapted to select secure optimal CH and trust based routing model which combines direct and indirect trust opinion values to resist routing attacks for establishing trustworthy and reliable routes towards base station.

TbDd: A new trust-based, DRL-driven framework for blockchain sharding in IoT

Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards, yet it is vulnerable to the 1% attacks where dishonest nodes target a shard to corrupt the entire blockchain. Balancing security with scalability is pivotal for such systems. Deep Reinforcement Learning (DRL) adeptly handles dynamic, complex systems and multi-dimensional optimization. This paper introduces a Trust-based and DRL-driven (TbDd) framework, crafted to counter collusion attack risks and dynamically adjust node allocation, enhancing throughput while maintaining network security. With a comprehensive trust evaluation mechanism, TbDd discerns node types and performs targeted resharding against potential threats. The TbDd framework maximizes the tolerance for dishonest nodes, optimizes node movement frequency, ensures even node distribution in shards, and balances sharding risks. Extensive evaluations validate TbDd’s superiority over conventional random-, community-, and trust-based sharding methods in shard risk equilibrium and reducing cross-shard transactions.

A trustworthy data collection scheme based on active spot-checking in UAV-Assisted WSNs

The UAV-WSN systems integrates the flexibility of UAVs with the communication and sensing capabilities of WSNs, making them important means of information monitoring, collection, and analysis in various fields that has received widespread attention. However, various malicious attacks pose threats to the security of UAV-WSN systems, resulting in serious vulnerabilities in trustworthy data collection. Therefore, in this article, we propose an Active Spot-Checking-based Trustworthy Data Collection (ASC-DC) scheme, which makes the following main contributions: (1) A novel UAV-assisted active and verifiable node trust evaluation framework is proposed. It includes an active trust acquisition mechanism based on spot-checked factors of nodes and a node trustworthy interaction mechanism based on Authoritative Information Tables (AIT). Through actively spot-check nodes using UAV, this framework can rapidly and accurately evaluate node trustworthiness while assisting sensor nodes in establishing trust relationships quickly. (2) A layered trust computation model is proposed, where the primary trust computation and node security supervision tasks are offloaded to more powerful UAV, thereby reducing information congestion and node energy consumption caused by trust evaluation in the WSN. (3) An improved Ant Colony Optimization (ACO) algorithm is proposed to efficiently acquire UAV’s spot-checking flight trajectories. It reduces the cost of spot-checking nodes using UAV and improves the efficiency of trust verification. Experimental results demonstrate that the proposed ASC-DC scheme can accurately evaluate node trustworthiness in an efficient way, ensuring reliable data collection in UAV-WSN systems.

A trustworthy data collection approach from sensor nodes using trust score of mobile vehicles for smart city

AbstractIn smart cities, a substantial amount of data is collected for analytics and a better life for the citizens. The schemes based on data collection through mobile vehicles (MV) and further verification of that data through unmanned aerial vehicles (UAV) are popular. Many trust‐based schemes of the MV have been proposed recently. However, these schemes suffered from recognition accuracy, judgment trust, and collusion attack problems. In this paper, we propose a Gompetz function‐based trust evaluation scheme. In this scheme, the direct trust of the MV is computed by comparing the data provided by the MV and the same reported by the UAV. Since the UAV can collect only limited data, indirect trust of the vehicle is computed by comparing the data reported by the MV and the same reported by the MV having the highest trust. We also applied the variable trust, which considers the recent Trust of the MVs. Then, combining all these trusts with significant weight, the final trust score of the MV is computed. After experimenting, our proposed scheme is more credible and removes the shortcomings of the existing methods by providing better recognition, accuracy, judgment, and trust.