Trust Threshold Research Articles

Consensus reaching with the dynamic hybrid trust network in group decision making based on an extensive bounded confidence

This study focuses on the consensus-reaching process (CRP) in social networks. Over the years, ample research has been conducted on CRP levels. However, most feedback mechanisms do not consider the decision maker’s (DM) willingness to accept the recommendations provided by these mechanisms. Therefore, this study proposes an extensive bounded confidence-based group consensus feedback mechanism that primarily consists of two parts: (a) the construction of a hybrid dynamic trust network and (b) the construction of an extensive bounded confidence model. In the first part, we propose an algorithm to calculate the importance of network nodes and apply it to the trust propagation and aggregation process. In the second part, we propose an extensive bounded confidence model to address the limitations of existing traditional bounded confidence models. In the proposed extensive bounded confidence model, the trust threshold is no longer a “switch” to determine whether a DM interacts with other DMs but a basis to determine the strength of the DMs’ interaction; moreover, the hybrid dynamic trust relationship will determine the negotiation sets. A practical case of site selection and comparative analysis verify the effectiveness and flexibility of the proposed mechanism.

Institutional distrust trap: an analysis of the effect of public distrust in the Nigeria Police Force

ABSTRACT This article explores an effect of distrust of the police – unwillingness to rely on them for security and justice – on policing dynamics in contemporary Nigeria using a qualitative research design. It argues that policing according to formal prescriptions such as the Criminal Procedure Act is virtually impossible in contemporary Nigeria because of this pervasive public distrust. At the same time, police officers resort to informal processes that further undermine public trust in the criminal justice system and create a self-defeating cycle that allows many to justify support for or active participation in illegal forms of crime control, including lynching by mobs and extra-legal killing by security forces. This proposition is significant for policing studies in two ways. Firstly, it suggests a trust threshold that, when breached, leads to policing collapse. Despite widespread distrust, many police forces maintain order. Secondly, it reveals how distrust perpetuates itself, even amid attempts at reform in various countries.

A Novel Approach for Gender Prediction through Fingerprint Analysis using Fuzzy C-Means Algorithm and Artificial Neural Network

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

Cognitive Reapportionment and the Art of Letting Go: A Theoretical Framework for the Allocation of Decision Rights

ABSTRACT We have entered an era where, in addition to us humans, systems can also think. It is imperative to decide how decision rights and authorities are allocated and distributed across interconnected co-cognitors: things that think. To address this question vital to the redesign of organizations, we reconceptualize cognitive reapportionment as the dynamic reallocation of decision rights and authorities across human and system co-cognitors capable of independent decision making. We articulate the Helix Model of Decision Journeys, wherein decision journeys, comprised of decision elements allocable to human or system co-cognitors, interweave and integrate business processes. The dynamic reapportionment of cognitive responsibilities across co-cognitors is dependent upon the type of scientific reasoning—deduction, induction, or abduction—at each decision element. We further propose two intertwined mechanisms for the art of letting go that facilitate the adoption of the Helix model. First, the nexus of omnipresent system co-cognitors collectively needs to evoke trust. Only when Technology Trust Thresholds are exceeded can decision making be reorganized and control relinquished to system co-cognitors. Second, the world of omnipresent cognitors will compel us to redesign, reprogram, and remix our workflow and business processes. Only through a purposeful effort to Redesign in the Remix Regime can the deduction-induction-abduction nature of decision tasks serve as the basis for their reapportionment.

A novel higher-order Deffuant–Weisbuch networks model incorporating the Susceptible Infected Recovered framework

The literature on opinion dynamics lacks consideration of the changing states of nodes during the process of opinion interaction, as observed in the literature on social networks. To address this gap, this paper proposes an adapted three-dimensional Deffuant–Weisbuch model based on the Susceptible Infected Recovered (SIR) model, which explores the impact of node state changes on opinion interaction. We incorporate the SIR model to describe changes in node state and improve the trust threshold by taking into account nodes’ emotional factor, cognitive ability, and state of the arts. The model is also modified using the opinion natural evolution coefficient and accounts for dynamic changes in social network structure. We validate our model with different network types and structures, and verify our results using the Louvain algorithm and index of centrality. Our model reveals that under different network types and structures, multiple opinion cliques are formed, and the opinions of ordinary nodes tend to converge around those of opinion leaders, indicating the guiding role of opinion leaders in shaping public opinion.

On incomplete matrix information completion methods and opinion evolution: Matrix factorization towards adjacency preferences

Completeness and consistency are widely acknowledged as indispensable prerequisites for applying fuzzy preference relations in resolving real-world problems. Interactions between decision-makers in the field of group decision-making can have a profound effect on preference changes, especially in situations of incomplete information. However, preference matrices generated using existing models often suffer from low consistency during opinion evolution, which further increases the risk of wrong decisions. To cope with these problems, this paper proposes an opinion evolution method under incomplete information. The method consists of two main components: (1) A preference prediction model based on matrix factorization algorithm and social trust networks under incomplete information, and (2) A model for decision-makers’ preference evolution under dynamic trust scenarios. In Part (1), to simplify the preference collection method and reduce the occurrence of inconsistent information, this paper presents the concept of a consistent adjacency fuzzy preference matrix. To advance the utilization of machine learning algorithms for consistent preference relations, this paper employs the matrix factorization method with stochastic gradient descent to predict missing preferences in adjacency fuzzy preference matrix. In Part (2), to address the shortcomings in past opinion evolution models, this paper simulates the change of decision-makers’ opinions in different stages of interactions under variable trust weights by using alternative ranking similarity and trust thresholds. To illustrate the practical application of the proposed model, this article uses the enterprise supplier selection problem as a case study. The model’s effectiveness and feasibility are then validated through a comparative analysis with existing methods.

A Blockchain-Based Trustworthy Access Control Scheme for Medical Data Sharing

Blockchain is commonly employed in access control to provide safe medical data exchange because of the characteristics of decentralization, nontamperability, and traceability. Patients share personal health data by granting access rights to users or medical institutions. The major purpose of the existing access control techniques is to identify users who are permitted to access medical data. They hardly ever recognize internal assailants from legitimate entities. Medical data will involve multilayer access within the authorized organizations. Considering the cost of permissions management and the problem of insider malicious node attacks, users hope to implement authorization constraints within the authorized institutions. It can prevent their data from being maliciously disclosed by end-users from different authorized healthcare domains. For the purpose to achieve the fine-grained permissions propagation control of medical data in sharing institutions, a trust-based authorization access control mechanism is suggested in this study. Trust thresholds are assigned to different privileges based on their sensitivity and used to generate zero-knowledge proof to be broadcasted among blockchain nodes. This method evaluates the trust of each user through the dynamic trust calculation model. And meanwhile, smart contract is employed to verify whether the user’s trust can activate some permissions and ensure the privacy of the user’s trust in the process of authorization verification. In addition, the authorization transaction between users and institutions is recorded on the blockchain for patient traceability and accountability. The feasibility and effectiveness of the scheme are demonstrated through comprehensive comparisons and extensive experiments.

Toward a Distributed Trust Management System for Misbehavior Detection in the Internet of Vehicles

Recent considerable state-of-the-art advancements within the automotive sector, coupled with an evolution of the promising paradigms of vehicle-to-everything communication and the Internet of Vehicles (IoV), have facilitated vehicles to generate and, accordingly, disseminate an enormous amount of safety-critical and non-safety infotainment data in a bid to guarantee a highly safe, convenient, and congestion-aware road transport. These dynamic networks require intelligent security measures to ensure that the malicious messages, along with the vehicles that disseminate them, are identified and subsequently eliminated in a timely manner so that they are not in a position to harm other vehicles. Failing to do so could jeopardize the entire network, leading to fatalities and injuries amongst road users. Several researchers, over the years, have envisaged conventional cryptographic-based solutions employing certificates and the public key infrastructure for enhancing the security of vehicular networks. Nevertheless, cryptographic-based solutions are not optimum for an IoV network primarily, since the cryptographic schemes could be susceptible to compromised trust authorities and insider attacks that are highly deceptive in nature and cannot be noticed immediately and are, therefore, capable of causing catastrophic damage. Accordingly, in this article, a distributed trust management system has been proposed that ascertains the trust of all the reputation segments within an IoV network. The envisaged system takes into consideration the salient characteristics of familiarity, i.e., assessed via a subjective logic approach, similarity, and timeliness to ascertain the weights of all the reputation segments. Furthermore, an intelligent trust threshold mechanism has been developed for the identification and eviction of the misbehaving vehicles. The experimental results suggest the advantages of our proposed IoV-based trust management system in terms of optimizing the misbehavior detection and its resilience to various sorts of attacks.

A multi-stage approach for design concept evaluation based on complex network and bounded confidence to achieve consensus

PurposeA successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.Design/methodology/approachThis paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.FindingsThe results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.Research limitations/implicationsUpdating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.Originality/valueThis method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs

Wireless Sensor Networks (WSNs) have been successfully utilized for developing various collaborative and intelligent applications that can provide comfortable and smart-economic life. This is because the majority of applications that employ WSNs for data sensing and monitoring purposes are in open practical environments, where security is often the first priority. In particular, the security and efficacy of WSNs are universal and inevitable issues. One of the most effective methods for increasing the lifetime of WSNs is clustering. In cluster-based WSNs, Cluster Heads (CHs) play a critical role; however, if the CHs are compromised, the gathered data loses its trustworthiness. Hence, trust-aware clustering techniques are crucial in a WSN to improve node-to-node communication as well as to enhance network security. In this work, a trust-enabled data-gathering technique based on the Sparrow Search Algorithm (SSA) for WSN-based applications, called DGTTSSA, is introduced. In DGTTSSA, the swarm-based SSA optimization algorithm is modified and adapted to develop a trust-aware CH selection method. A fitness function is created based on the nodes’ remaining energy and trust values in order to choose more efficient and trustworthy CHs. Moreover, predefined energy and trust threshold values are taken into account and are dynamically adjusted to accommodate the changes in the network. The proposed DGTTSSA and the state-of-the-art algorithms are evaluated in terms of the Stability and Instability Period, Reliability, CHs Average Trust Value, Average Residual Energy, and Network Lifetime. The simulation results indicate that DGTTSSA selects the most trustworthy nodes as CHs and offers a significantly longer network lifetime than previous efforts in the literature. Moreover, DGTTSSA improves the instability period compared to LEACH-TM, ETCHS, eeTMFGA, and E-LEACH up to 90%, 80%, 79%, 92%, respectively, when BS is located at the center, up to 84%, 71%, 47%, 73%, respectively, when BS is located at the corner, and up to 81%, 58%, 39%, 25%, respectively, when BS is located outside the network.

Opinion Evolution with Information Quality of Public Person and Mass Acceptance Threshold.

Public persons are nodes with high attention to public events, and their opinions can directly affect the development on events. However, because of rationality, the followers' acceptance to the public persons' opinions will depend on the information trait on public persons' opinions and own comprehension. To study how different opinions of the public persons guide different followers, we build an opinion dynamics model, which would provide a theoretical method for public opinion management. Based on the classical bounded confidence model, we extract the information quality variables and individual trust threshold and introduce them to construct our two-stage opinion evolution model. And then in the simulation experiments, we analyze the different effects of opinion information quality, opinion release time, and frequency on public opinion by adjusting the different parameters. Finally, we added a case to compare real data, the data from classical model simulation and the data from improved model simulation to verify the effectiveness on our model. The research found that the more sufficient the argument and the more moderate the attitude, the more likely to guide the public opinion. If public person holds different opinions and different information quality, he should choose different time to present his opinion to achieve ideal guide effect. When public person holds neutral opinion and the information quality is relatively general, he/she can intervene in public opinion as soon as possible to control final public opinion; when public person holds extreme opinion and the information quality is relatively high, he/she can choose to express opinion after a certain period on public opinion evolution, which is conducive to improve the guidance effect on public opinion. The frequency of releasing opinions of public person consistently has a positive impact on the final public opinion.

Adaptive Trust Threshold Model Based on Reinforcement Learning in Cooperative Spectrum Sensing

In cognitive radio systems, cooperative spectrum sensing (CSS) can effectively improve the sensing performance of the system. At the same time, it also provides opportunities for malicious users (MUs) to launch spectrum-sensing data falsification (SSDF) attacks. This paper proposes an adaptive trust threshold model based on a reinforcement learning (ATTR) algorithm for ordinary SSDF attacks and intelligent SSDF attacks. By learning the attack strategies of different malicious users, different trust thresholds are set for honest and malicious users collaborating within a network. The simulation results show that our ATTR algorithm can filter out a set of trusted users, eliminate the influence of malicious users, and improve the detection performance of the system.

Energy efficient routing with secure and adaptive trust threshold approach in mobile ad hoc networks

Energy efficient routing with secure and adaptive trust threshold approach in mobile ad hoc networks

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles.

The rapid proliferation of the emerging yet promising notion of the Internet-of-Vehicles (IoV) has led to the development of a variety of conventional trust assessment schemes to tackle insider attackers. The primary reliance of these frameworks is on the accumulation of individual trust attributes. While aggregating these influential parameters, weights are often associated with each individual attribute to reflect its impact on the final trust score. It is of paramount importance that such weights be precise to lead to an accurate trust assessment. Moreover, the value of the minimum acceptable trust threshold employed for the identification of dishonest vehicles needs to be carefully defined to avoid delayed or erroneous detection. This paper employs an IoT data set from CRAWDAD by suitably transforming it into an IoV format. This data set encompasses information regarding 18,226 interactions among 76 nodes, both honest and dishonest. First, the influencing parameters (i.e., packet delivery ratio, familiarity, timeliness and interaction frequency) were computed, and two feature matrices were formed. The first matrix (FM1) takes into account all the pairwise individual parameters as individual features, whereas the second matrix (FM2) considers the average of all pairwise computations performed for each individual parameter as one feature. Subsequently, unsupervised learning is employed to achieve the ground truth prior to applying supervised machine learning algorithms for classification purposes. It is worth noting that Subspace KNN yielded a perfect precision, recall, and the F1-score equal to 1 for individual parametric scores, whereas Subspace Discriminant returned an ideal precision, recall, and the F1-score equal to 1 for mean parametric scores. It is also evident from extensive simulations that FM2 yielded more accurate classification results compared to FM1. Furthermore, decision boundaries among honest and dishonest vehicles have also been computed for respective feature matrices.

Hybrid Authentication Using Node Trustworthy to Detect Vulnerable Nodes

An ad-hoc sensor network (ASN) is a group of sensing nodes that transmit data over a wireless link to a target node, direct or indirect, through a series of nodes. ASN becomes a high-risk group for several security exploits due to the sensor node’s limited resources. Internal threats are more challenging to protect against than external attacks. The nodes are grouped, and calculate each node’s trust level. The trust level is the result of combining internal and external trust degrees. Cluster heads (CH) are chosen based on the anticipated trust levels. The communications are then digitally signed by the source, encoded using a key pair given by a trustworthy CH, decoded by the recipient, and supervised by verifications. It authenticates the technique by identifying the presence of both the transmitter and the recipient. Our approach looks for a trustworthy neighboring node that meets the trust threshold condition to authenticate the key produced. The companion node reaffirms the node’s reliability by getting the public-key certification. The seeking sensor node and the certification issuer node must have a close and trusting relationship. The results of the proposed hybrid authentication using a node trustworthy (HANT) system are modeled and tested, and the suggested approach outperforms conventional trust-based approaches in throughput, latency, lifetime, and vulnerability methods.

Certificate revocation schemes for providing secure QoS in MANETs

SummaryDue to crucial exchange issues, security in mobile ad hoc networks (MANETs) is more complicated. It is vital to secure MANET exchanges to ensure the network's services development. In this research, the proposed model focuses on providing trust and certificate‐based Malicious Node detection for mobile adhoc networks and improving certificate revocation based on cluster head. The cluster head is crucial in eliminating falsely accused nodes. The malicious node may be easily discovered and isolated from the network using the trusted value. If the trust value falls below the trust threshold value in a trust management, the node is identified as malicious, the certificate is canceled, and the node is isolated from the network. The TSQRS protocol's integration with the CRS scheme was the main emphasis of the suggested model. The proposed model also creates more stable clusters with the help of Trust manager. When compared to other current protocols and methodologies, the findings produced by the suggested approach for the TSQRS‐CRS scheme improve performance and stability in the QoS of MANETs. Finally, certain parameters were used to run the simulation, which resulted in an improved Packet Delivery Ratio, Routing Overhead, and reduced Delay.

Substation Operation Information Maintenance Based on Intelligent Data Mining

In order to solve the problems of redundancy of substation operation information, diversification of monitoring system versions, and heterogeneous data, the author proposes a remote-based intelligent data mining system for substation automation. Use data mining technology to classify and standardize the original alarm data, and associate it with the equipment ledger system to obtain a data warehouse of equipment parameters, status information, and related historical fault information. On this basis, the improved Apriori algorithm is used to extract strong association rules that satisfy the minimum trust threshold from the data warehouse, which provides decision-making basis for the operation and maintenance management of substation equipment. Experimental results show that using the improved Apriori algorithm to perform data mining on the substation operation alarm information in 143 intervals of a power supply bureau substation, the following strong association rules are obtained: association rule 1, “handcart test position ∩ device running alarm ∩ alarm signal has been reset interval debugging and maintenance”, γ support = 57 % , γ confidence = 69 % , and association rule 2, “Protection action∩ switch sectional position ∩ reclosing action ∩ total accident (hold) ∩ switch sectional signal has been reset ∩ reclosing action signal has been reset ∩ total accident (hold) signal has been reset Temporary equipment failure”, γ support = 41 % , γ confidence = 79 % . The effectiveness of the proposed data mining method is verified by a field case.

Corpus-Based Data Acquisition and Topic Analysis of Chinese-Related Public Opinion in Western Media

This paper sorts out and discusses the different methods used in the analysis of China-related public opinion, builds a theoretical system of public opinion analysis methods on this basis, conducts case analysis and research on hot public opinion combined with corpus, and selects the semantic analysis method from the technical level supporting the public opinion analysis method. From the perspective of related technologies involving intelligent analysis methods, comparative analysis and improved applications are given, which provides an effective analysis basis for government public management departments to fully grasp and guide China-related public opinion. Sentiment tendency analysis is also an important direction of Chinese-related public opinion research. This paper sorts and analyzes China-related public opinion from the characteristics, applications, and text processing methods of different granularities and builds and improves the annotation model for the smallest granularity. The frequency, trend, and evolution characteristics of China-related public opinion events are sorted out and analyzed, and the trend analysis method is used to analyze the evolution trend of attention of public opinion events. The distribution of individual opinion acceptance, trust threshold, and opinion leaders are simulated by experiments. The impact on the evolution of China-related public opinion was determined. The experimental results show that the effect of the method proposed in this paper is improved by about 10% compared with the direct use of statistical learning methods for emotional orientation analysis. The SVM method also has obvious advantages over the Bayesian method, especially the combination of the SVM method and the bigram method which gives the best results.

A Trust-Driven Contract Incentive Scheme for Mobile Crowd-Sensing Networks

By leveraging the power of crowd, the prevalence of mobile devices in mobile crowd-sensing (MCS) networks helps and provides a wide range of sensing services through collecting and sharing sensing data. However, due to the diverse behaviours of mobile users, unreliable or malicious users and platforms could provide untrusted data, which affects the quality of sensing service. Besides, mobile users are reluctant to participate in sensing tasks without sufficient incentives. It is desirable to design a trust and incentive scheme to improve the service efficiency of MCS. In this paper, we propose a novel trust-driven contract incentive framework in MCS, which guarantees the service quality and stimulates mobile users to join sensing tasks. We first design a trust evaluation scheme between mobile users and sensing platforms based on the historical interactions to derive the reliability value of sensing platform. Then, the trust threshold is formulated to filter out malicious sensing platforms. By considering the privacy preferences of mobile users, we establish a contract incentive scheme to maximize the utility of both mobile users and sensing platforms. The design objective is to derive a set of optimal contracts under both discrete and continuous contract models. Meanwhile, the designed contracts guarantee the individual rationality (IR) and incentive compatibility (IC) properties. Finally, simulations are conducted to evaluate the effectiveness of the proposed trust-driven contract incentive scheme, and results demonstrate that the proposed scheme can jointly improve the quality of sensing service and maximize the utilities of mobile users and sensing platforms.