Trust Evaluation Research Articles

A SYSTEMATIC LITERATURE REVIEW ON SECURITY ISSUES IN CLOUD COMPUTING USING EDGE COMPUTING AND BLOCKCHAIN: THREAT, MITIGATION, AND FUTURE TRENDS

This Systematic Literature Review (SLR) examines the security threats experienced by cloud computing along with QoS by edge computing and scheduling. Various consumers and cloud service providers (CSP) obtained benefits from the cloud computing environment. However, various privacy and security challenges occur in this digital age. This SLR mainly aims to review the state of the art research studies on secure cloud computing with high QoS and its challenges. We examined numerous research studies related to cloud computing security with secure service level agreement (SLA) which are published between 2019 to 2022 from the reputed electronic databases. Cloud computing security is ensured mostly based on trust evaluation, implementation of blockchain technology, authentication, and cryptography techniques, and implementing edge computing in the cloud environment. This SLR also analyzed the research gaps of the previous research papers and provides future research directions to obtain secure cloud computing environment with high QoS performance.

METAMORPHOSES OF THE SOCIO-ECONOMIC WELL-BEING OF THE REGION (USING THE EXAMPLE OF THE UDMURT REPUBLIC)

The article analyzes the socio-economic processes occurring in various regions of our country, their heterogeneity and ambiguity.These phenomena are becoming the subject of scientific debate, in which the emphasis is placed on the fact that the situation in the regions is constantly assessed by both government and independent structures and, it would seem, in such conditions society can count on an objective assessment of the activities of governors and understand their effectiveness as managers and trust official information and ratings, which are published with enviable consistency, and not their feelings and “conversations in the kitchen.” The material notes the difference between society’s assessment and official statistics, real life and reports from regional leadership. One gets the impression that we are not talking about the same subjects and not about the same indicators characterizing the degree of change in the situation in the economy and social life of the territory, on the basis of which an assessment of the effectiveness of management and conclusions should be made on the advisability of further extending the term of management or making a decision on major adjustments to the territory development strategy. The author questions the correctness of the current elements of assessing the ongoing socio-economic processes in the territories, since they do not fully reflect the real picture.Officially adopted methods can and should be refined taking into account empirical experience and the conclusions of scientists, and the results obtained should give an objective assessment with an appropriate reaction from higher structures.

Interact: A visual what-if analysis tool for virtual product design

Virtual prototyping is increasingly used by businesses to streamline operations, cut costs, and enhance daily operations. This often includes a variety of modeling techniques among which, complex, black-box models. The path from model development to utilization in applied contexts is yet long. Domain experts need to be convinced of the validity of the models and to trust their predictions. To be used in the field, model capabilities need to be affordable, that is, allow rapid and interactive scenario building, even for non-experts. Complex relations governed by statistical interactions must be unveiled for users to understand unexpected predictions. We propose Interact, a model-agnostic, visual what-if tool for regression problems, supporting (1) the visualization of statistical interactions between features, (2) the creation of interactive what-if scenarios using predictive models, (3) the evaluation of model quality and building trust, and (4) the externalization of knowledge through model explainability. While the approach applies in various industrial contexts, we validate the application purpose and design with a detailed case study and a qualitative user study with engineers in the tire industry. By unraveling statistical interactions between features, the INTERACT tool proves to be useful to increase the transparency of black-box machine learning models. We also reflect on lessons learned concerning the development of visual what-if tools for virtual product development and beyond.

“Do Good and Be ‘Liked’”: CSR Messages on Social Media during COVID-19 Pandemic and Consumer Responses

This study explores the types of corporate social responsibility (CSR) messages communicated on social media during the outbreak of COVID-19 in the United States and its corresponding effects in driving consumer engagement and related psychological responses. Two studies were conducted. In Study 1, we analyzed corporations’ social media posts. We identified and compared four different CSR message types (i.e., employee-relevant CSR, community-relevant CSR, promotional CSR, and company statement) in their effects on consumers’ behavioral engagement. In Study 2, we conducted an online experiment to extend the findings of Study 1 further. Study 1 revealed that employee-relevant CSR received the highest consumer behavioral engagement among all types of CSR messages. Study 2 revealed a mediation effect of consumers’ engagement intention between the CSR message types and consumers’ brand attitudes and trust. In addition, community-relevant CSR and company statements received higher evaluations of brand attitude and brand trust.

An electronic medical record access control model based on intuitionistic fuzzy trust

Electronic Medical Records (EMR), as the core data of medical big data, has improved the efficiency of medical services in the process of sharing and use, but a series of data privacy leakage problems have emerged. Access control technology can ensure users' legitimate and appropriate access to data resources, but traditional access control has many limitations such as static and coarse-grained, which is difficult to meet in the dynamic and complex healthcare environment. We create a model to solve the privacy leakage problem of healthcare big data. The model utilizes the Latent Delicacy Allocation (LDA) topic model to quickly search documents and extract the topic probability distributions between the target patient and other patients' electronic medical records, which improves the efficiency of medical record document analysis; Calculating physician access similarity, we quantify physician access behavior to more accurately describe physician access behavior in the form of data. We combined the intuitionistic fuzzy theory with the trust evaluation method to create a novel trust evaluation method-- Intuitionistic Fuzzy Trust Evaluation Method, which can reflect the uncertainty characteristics that exist in the physician's diagnostic and treatment process when quantifying the trust of the access by using this method, and it is more flexible and practical, which enhances the accuracy of the quantification of the trust. Improving the traditional trust aggregation method by adding a time window and time decay function, the optimized method improves the accuracy of trust aggregation, and the experimental results are more in line with the actual law. We created a new intuitionistic fuzzy trust access control model using a relative entropy-based intuitionistic fuzzy clustering algorithm and a reward and punishment mechanism to achieve adaptive and dynamic access control for physician access behavior. Experiments show that the access control model based on intuitionistic fuzzy trust proposed in this article achieves 95% correctness in identifying user access behaviors and 94.29% correctness in identifying excessive user access behaviors. Experiments have proved that this model has obvious advantages over other models in the process of privacy protection of electronic medical record systems and has certain control effects.

DTC-MDD: A spatiotemporal data acquisition technology for privacy-preserving in MCS

Some attackers in the Internet of Things submit falsified high-quality data to cause harm to users. To prevent malicious workers from reporting untruthful data for skyline computation in Mobile Crowd Sensing, we propose a double trust check-based spatiotemporal data acquisition scheme, DTC-MDD. In DTC-MDD, worker trust uses four-way validation to obtain reliable worker trust evaluations. Then, based on Probabilistic Skyline Calculation, we propose a worker selection algorithm to select high-trust, high-quality workers for data reporting. We also introduce the Non-Interactive Encrypted Integer Comparison Protocol to safeguard privacy between workers and users from malicious attacks. Finally, through extensive simulations on real datasets, DTC-MDD effectively enhances the quality and security of spatiotemporal data acquisition. DTC-MDD improved the data quality and reliability of candidate worker sets by 16.2% and 49.1%, respectively, and the data quality and reliability of the first skyline worker by 21.4% and 320.0%, respectively.

Moving beyond the political trust crisis debate: Residual analyses to understand trends in political trust

AbstractFor decades, political scientists have hotly debated longitudinal trends in political trust rates. An important undercurrent in the debate is that any decline in political trust might signal a legitimacy crisis. Yet, descriptive figures are unable to distinguish between two interpretations of these downward trends: (i) declines that can reasonably be expected as a reflection of declining political trustworthiness (i.e., procedural or output performance) and thereby reflect critical citizens who monitor their democratic institutions; and (ii) downward trends that are not warranted by democratic performance and thereby suggest a more fundamental disconnect between citizens and their democratic institutions.This research note argues that residuals to multilevel models of political trust allow us to distinguish between these two types, and thereby provide a better understanding of trends in political trust. These residuals do not only reveal short‐term aberrations to the explanatory model (often reflecting short‐lived, country‐specific events), but also the extent to which a country's trust rate systematically underperforms in the middle‐ to long‐term. To the extent that declining trust rates are lower than explanatory models predict, the residuals express excessive distrust. To the extent that declining trust rates are in line with the explanatory models, the residuals reflect critical, monitoring citizenship.We outline the approach of residual analyses as a tool to better understand trends in political trust. We illustrate the use of these residual analyses on a cross‐national, longitudinal data set (the Eurobarometer), covering 15 Western and Southern European countries between 1999 and 2019. While political trust rates fluctuate in all these countries, we only find evidence for a structural decline in two of these countries. In France and Spain political trust failed to recover in line with improving economic and institutional performance after the Great Recession. We then test the versatility of the tool to different conditions, including retests on an alternative set of countries (11 Central and Eastern European countries between 2004 and 2019) and an alternative dataset with different measures and time points (the European Social Survey).Finally, we elaborate on the two main conditions under which residual analyses offer a useful tool to the trend debate in political trust research: (1) a firm understanding of the object‐driven determinants of political trust, and (2) a detailed coverage of country‐wave combinations to separate structural trends from short‐term fluctuations.

Scrutinize Analysis of Crime Investigation – Assigned Weighted Approach

This study focuses on trust rating between police records and observed current case’s data, employing a weighted graph approach to make informed decisions in different criminal investigations. Four distinct cases, ranging from theft to homicide, were examined, each with its own assigned trust levels. The outcomes of these cases varied, with trust ratings playing a significant role in influencing the final decisions made by law enforcement agencies. The results underscore the importance of considering specific evidence and circumstances in the determination of suspects guilt or innocence in AI based outputs

Інтелектуальна система ідентифікації рейтингу довіри користувача

The article develops an intelligent system for identifying the user's trust rating, which allows viewing information about contacts that are more aimed at creating trust in the interlocutor or providing information that helps to identify the caller or the person with whom we are trying to contact. The k-nearest neighbours method was chosen to create the recommendation system. The main advantage of using the k-nearest neighbours method is the ability to take into account the unique trust rating of each phone number. It is important not only to find phone numbers with a similar rating, but also to take into account the approximate rating. The k-nearest neighbours method allows you to provide recommendations based on the similarity between phone numbers that have received positive feedback from users with similar preferences. Intelligent recommendation systems can provide phone numbers with similar ratings. When a user requests information about a phone number associated with fraudsters, the system instantly offers low-rated phone numbers that are therefore potential fraudsters.

Trust evaluation model of power terminal based on equipment portrait

As the number of power terminals continues to increase and their usage becomes more widespread, the security of power systems is under great threat. In response to the lack of effective trust evaluation methods for terminals, we propose a trust evaluation model based on equipment portraits for power terminals. First, we propose an exception evaluation method based on the network flow order and evaluate anomalous terminals by monitoring the external characteristics of network traffic. Second, we propose an exception evaluation method based on syntax and semantics. The key fields of each message are extracted, and the frequency of keywords in the message is statistically analyzed to obtain the keyword frequency and time-slot threshold for evaluating the status of the terminal. Thus, by combining the network flow order, syntax, and semantic analysis, an equipment portrait can be constructed to guarantee security of the power network terminals. We then propose a trust evaluation method based on an equipment portrait to calculate the trust values in real time. Finally, the experimental results of terminal anomaly detection show that the proposed model has a higher detection rate and lower false detection rate, as well as a higher real-time performance, which is more suitable for power terminals.

A Trust Model Based on Characteristic Factors and SLAs for Cloud Environments

In view of the lack of research regarding characteristic factors (e.g., service cost, quality, etc.) in trust models for cloud environments, as well as the lack of negotiation and monitoring mechanisms, a trust model based on characteristic factors and service level agreements (SLAs) is proposed in this article. First, on the basis of comprehensive trust and self-recommended trust, we introduce the cost deviation trust and service quality coefficient, to simultaneously consider the influence of common and characteristic factors upon trust evaluations and thereby improve the accuracy of trust evaluations. Second, we establish a negotiation and monitoring mechanism whereby both parties sign an SLA before the trade and require SLA agent monitoring services to improve the accuracy of the service cost and quality evaluation and the efficiency of malicious entity identification. Finally, using the agreement quality, experience quality, and monitoring quality, we more accurately judge the trade result and update the recognition degree (which plays a key role in trust evaluation), to further improve the accuracy of trust evaluation and thereby the trade success rate. The results of experiments conducted upon real datasets show that our model can effectively resist spoofing, coordination, and defamation attacks from malicious entities, and it offers a high trade success rate.

Swarm Intelligence-Optimized Energy Management for Prolonging the Lifetime of Wireless Sensor Networks

Recent technological and industrial progress has enabled the development of small, high-performing, energy-saving, affordable sensor nodes that possess the potential to adapt, be self-aware, and self-organize. These nodes are designed for versatile communications applications. Sensor networks for sustainable development focus on the ways in which sensor network technology can enhance social development and improve living standards without causing harm to the environment or depleting natural resources. Wireless sensor networks (WSNs) offer undeniable benefits in various fields, including the military, healthcare, traffic monitoring, and remote image sensing. Given the constraints of sensor networks, varying degrees of security are necessary for these critical applications, posing difficulties in the implementation of conventional algorithms. The issue of security has emerged as a primary concern in the context of IoT and smart city applications. Sensor networks are often regarded as the fundamental building blocks of IoTs and smart cities. The WSN encompasses a routing algorithm, network strength, packet loss, energy loss, and various other intricate considerations. The WSN also addresses intricate matters such as energy usage, a proficient approach for picking cluster heads, and various other concerns. The recent growth of Wireless Sensor Networks (WSNs) has made it increasingly difficult to ensure the trustworthiness and reliability of data due to the distinct features and limitations of nodes. Hostile nodes can easily damage the integrity of the network by inserting fake and malicious data, as well as launching internal attacks. Trust-based security is employed to detect and identify rogue nodes, providing a robust and adaptable protection mechanism. Trust evaluation models are crucial security-enhancement mechanisms that enhance the reliability and collaboration of sensor nodes in wireless sensor networks. This study recommends the use of DFA UTrust, a unique trust technique, to effectively satisfy the security requirements of WSNs.

Attribute and User Trust Score-Based Zero Trust Access Control Model in IoV

The Internet of Vehicles (IoV) is an innovative area of interest in modern mobility that is rapidly evolving while facing complex challenges. Traditional IoV networks are susceptible to intrusion threats, which can lead to data leakage and seizure of vehicle control by attackers, thereby endangering vehicle users’ privacy and personal safety. An Attribute and User Trust Score-based Zero Trust Access Control Model (AU-ZTAC) is proposed, combining the zero-trust and attribute-based access control models to meet network protection requirements while achieving fine-grained dynamic access control and incorporating trust evaluation in the access control process to better reflect users’ intent. Experimental results demonstrate the effectiveness and feasibility of trust assessment through the proposed model. A comparison with the classical schemes illustrates that AU-ZTAC allows for more flexible and fine-grained access control in complex access control environments while improving IoV security.

Hybrid Multi-Objective-Derived Horse Herd and Dragonfly Algorithm-Based Energy-Efficient Secured Routing in WSN

Energy efficiency and security have become prominent aspects of Wireless Sensor Networks (WSNs) for transmitting data. The major challenge is to increase the “Quality of Service” (QoS) since it is restricted by fewer constrained resources. Although the existing routing protocols acquire efficient transmission, they are still subsisting with the challenges of improving security and energy conservation. It will be helpful in practical implications like the military, weather forecasting and healthcare industry. As the nodes are operated by the energy preservation of the battery, developing an energy-efficient protocol is challenging. Since WSN possesses massive numbers of sensor nodes, it also has to avoid long-term communication, and the clustering mechanism is a prerequisite. Considering the diverse objectives like delay, energy and distance, there are still remarkable challenges to develop the routing protocol. Moreover, security enhancement becomes another challenging issue over the network for data transmission through the sensor nodes. To overcome these issues, a novel energy-efficient routing model is designed in this paper by using a hybrid algorithm and the multi-objective derivatives. Initially, the clustering approach is employed to reduce the complexity and improve the security in an energy-efficient manner. Therefore, the Cluster Head (CH) is selected significantly using the novel Hybrid Horse Herd-Dragonfly Optimisation (HHHDO). The hybrid algorithm is developed by integrating Horse Herd Optimisation (HHO) and Dragonfly Algorithm (DA). Here, the optimal CH selection is achieved by deriving the multi-objective function regarding “distance, delay, energy, load and trust of nodes”. Trust is measured with the Neural Network (NN)-based HHHDO (NN-HHHDO) model. Depending on the trust evaluation and energy efficiency, the proposed model obtains better communication between the nodes. In the end, the experimentation of the recommended model is made using various measures and it’s ensured that the suggested technique enhances trust among the nodes and energy conservation in comparison to the existing algorithms.

Blockchain-based Distributed Financial Trust Evaluation System and Operation Mechanism

The functioning of the present-day financial system relies on the movement of funds and information. DeFi (Decentralized Finance), utilising blockchain technology, possesses the capability to establish autonomous and trustworthy smart contracts that do not require the involvement of intermediaries for escrow and auditing purposes. This results in the creation of a financial system that is characterised by its openness and transparency, devoid of any form of identity-based discrimination, and accessible to all individuals. The utilisation of a private blockchain system facilitates enhanced efficiency, while concurrently ensuring the attainment of privacy and security objectives. Moreover, such a system aligns more closely with prevailing regulatory standards. The Coalition Chain has the characteristic of a scale-free network, with its nodes following a power law distribution. This work aims to investigate credit governance from the standpoint of group behaviour and provide optimisation solutions accordingly.

Trust-Aware and Fuzzy Logic-Based Reliable Layering Routing Protocol for Underwater Acoustic Networks

Routing protocols based on trust mechanisms have been widely investigated for wireless sensor networks, and the works have achieved good results, while there are few works on trusted routing for underwater acoustic networks (UANs). However, trust-aware routing is the key to improving the packet delivery rate and the energy efficiency of UANs. Therefore, inspired by the theory of trust evaluation, a trust-aware and fuzzy logic-based reliable layering routing protocol (TAFLRLR) is proposed. In the TAFLRLR protocol, to avoid the problem of the void area and improve the transmission reliability, the candidate nodes of the next-hop forwarding nodes are determined according to the layers of neighbor nodes. Moreover, a fuzzy logic-based trust evaluation mechanism (FLTEM) is provided, which employs the fuzzy comprehensive evaluation decision model to calculate the comprehensive trust value for underwater sensor nodes. Further, the node density of a candidate node and its comprehensive trust value are taken as the input of a fuzzy control system and the forwarding probability (FP) of the node is taken as the output, and the candidate node with the highest FP is selected as the best forwarding node. Simulation results illustrate the superiority and effectiveness of the TAFLRLR protocol in terms of energy efficiency, routing reliability, and transmission reliability.

A dynamic anonymous authentication scheme with trusted fog computing in V2G networks

As an important component of smart grid, vehicle-to-grid (V2G) is helpful to improve grid stability, save on generation costs, and reduce energy consumption. However, the existing V2G authentication schemes focus on the trustworthiness of electric vehicles (EVs), instead of the trustworthiness of fog servers (FSs). For unsuspecting EVs, malicious FSs may steal their sensitive data and compromise privacy. We propose a dynamic anonymous authentication scheme with trusted fog computing in V2G networks. In our scheme, to ensure the trustworthiness of the FSs in V2G networks, a collaborative trust evaluation model among FSs is designed. The trust value of FSs can be calculated by comprehensively analyzing the timeliness, authenticity, and execution of FSs' behavior in the collaboration process. Meanwhile, trust evaluation is incorporated into the dynamic anonymous authentication scheme against malicious FSs and ensure a trusted environment of EVs in the authentication process. During the authentication process, to avoid the key escrow and computational overhead of FSs, the certificateless mechanism and aggregated signatures of non-linear pairs are used. We use a combination of long-term anonymity (LID) and temporary anonymity (TID) to ensure the anonymity and the signatures freshness of EVs. Finally, security proofs based on random oracle models and non-formal security analysis show that our scheme satisfies seven security requirements and resists five common attacks. The simulation results show that, in comparison with the five reference schemes, our scheme reduces average computational and communication overheads by 80.05% and 47.68%, respectively.

How anti-corruption actions win hearts: The evaluation of anti-corruption performance, social inequality and political trust—Evidence from the Asian Barometer Survey and the Latino Barometer Survey

Corruption is seen as a political cancer that erodes the public’s trust in political system. While it is generally agreed that corruption and political trust are negatively correlated, few researchers have explored the impact of anti-corruption performance on political trust. In this article, we focus on whether combating corruption can enhance political trust and how this influencing mechanism is realized. Based on analyses of the Asian Barometer Survey and the Latino Barometer Survey, we found that political trust is affected by the evaluation of anti-corruption performance and social inequality. The evaluation of anti-corruption performance can enhance political trust directly, while social inequality undermines political trust directly. Social inequality can also moderate the positive effect of the evaluation of anti-corruption performance on political trust. This study not only fills the previous research gap in the relationship between anti-corruption and political trust but also has great practical significance.

Trust-Based Routing Selection Policy on Mobile Ad-Hoc Network Using Aodv Routing Protocol

This study presents an enhanced Ad-hoc On-demand Distance Vector (AODV) routing protocol, termed Proposed_TAODV, designed to improve security in Mobile Ad-Hoc Networks (MANETs) of 150 nodes by incorporating trust-based mechanisms. Through a comprehensive simulation, the Proposed_TAODV is evaluated against existing AODV and Dynamic Source Routing (DSR) protocols under conditions of increasing malicious node presence. The results reveal that Proposed_TAODV maintains a higher Packet Delivery Ratio, experiences lower Average End-to-End Delay, and achieves greater Throughput compared to the benchmarks, indicating its superior performance and robustness. The integration of Direct Trust Evaluation, Indirect Trust Evaluation, and Trust Aggregation methods into the AODV protocol clearly enhances the MANET's resilience to security threats, establishing the Proposed_TAODV as a promising approach for securing MANETs against various forms of attacks and network disruptions.

Privacy-preserving trust management method based on blockchain for cross-domain industrial IoT

The Industrial Internet of Things (IIOT) contains many devices from different autonomous domains (e.g., factories), which need to cooperate to complete complex manufacturing process. Devices from different domains collaborate with each other, which greatly raises trust concerns about device-to-device interactions. Existing trust management approaches may result in the risk of privacy leakage and low accuracy of trust evaluation. Thus, trust issues during interaction remain unsolved but imperative. In this paper, we propose a blockchain-based privacy-preserving trust management architecture PPTMA. Specifically, PPTMA adopts federated learning to train a task-specific trust model for different collaborative task. Our work is the first attempt to research the relationship between the weight calculation of trust metric and the change of context in the field of trust management. To preserve the privacy of devices, differential privacy (DP) is exploited during the trust evaluation process. In addition, a game theory-based incentive mechanism is proposed to encourage the IIOT device for actively and honestly submitting the trust data into the blockchain as so to promote the accuracy of trust computing. Finally, we also design a parallel consensus protocol (OPBFT) which realizes an assembly line to speed up the efficiency of the consensus process. The idea of consensus assembly line firstly proposed by us brings new opportunities for improving the consensus efficiency. Extensive experiments have been conducted to show the effectiveness and efficiency of the proposed method.