scholarly journals A Comprehensive Trust Model Based on Social Relationship and Transaction Attributes

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yonghua Gong ◽  
Lei Chen ◽  
Tinghuai Ma
Keyword(s):  
Social Relationships ◽  
Social Relationship ◽  
Trust Model ◽  
Personal Interest ◽  
Social Commerce ◽  
Trust Evaluation ◽  
Reputation Mechanism ◽  
Model Based ◽  
Trust And Reputation ◽  
Proposed Model

The existing approaches to predict trust values in social commerce are based on personal social relationships without considering historical transaction information about products in social commerce, which results in false recommendations, and deceptions cannot be differentiated. Trust values extracted from social links can improve the performance of trust and reputation mechanism, but the rates from these links in social commerce can be false because of the stakeholders’ manipulation for personal interest. And the rates are also dynamic and inconsistent. Therefore, this paper proposes a comprehensive trust model by fully exploiting the effects of the transaction attributes and social relationships on users’ trust. The proposed model refines the granularity of trust evaluation and improves the discrimination of recommended information. Experiments demonstrate that the proposed model performs better and predicts more accurately than the three models compared under the same circumstance.

Trust Management Model based on Fuzzy Approach for Ubiquitous Computing

2016 ◽  
Vol 7 (2) ◽  
pp. 33-46 ◽  
Author(s):  
Nalini A. Mhetre ◽  
Arvind V. Deshpande ◽  
Parikshit Narendra Mahalle
Keyword(s):  
Ubiquitous Computing ◽  
Trust Management ◽  
Future Internet ◽  
Trust Model ◽  
Model Based ◽  
Trust Relationships ◽  
Current State ◽  
Proposed Model ◽  
Security Properties ◽  
Trust Models

The current state of ubiquitous computing has been greatly influenced by emerging networking developments like Internet of Things (IoT), Future Internet etc. Adequate trust management is crucial to provide security. The entities involved in communication must be trusted for specific purposes depending on their role. Using trust model, devices can run trust computations and guide their behaviors. To this effect, a method is needed to evaluate the level of trust between devices. Trust models investigated so far discusses that devices face problems when communicating as transforming trust relationships from real to virtual world requires the negotiation of trust based on the security properties of devices. However, these models are developed in limited devices. This paper proposes a distributed trust model for device-to-device communication in ubiquitous computing. Mathematical model based on fuzzy rules to establish trust is presented. Fuzzy simulation of the model is presented to validate the findings. Simulation results show that proposed model calculates fuzzy trust values reliably.

Introduction to the Investigating in Neural Trust and Multi Agent Systems

2017 ◽  
pp. 269-273 ◽  
Author(s):  
Gehao Lu ◽  
Joan Lu
Keyword(s):  
Trust Model ◽  
Self Organizing Map ◽  
Multi Agent Systems ◽  
Test Bed ◽  
Computational Trust ◽  
Agent Systems ◽  
Reputation Mechanism ◽  
Trust And Reputation ◽  
Multi Agent ◽  
Propagation Methods

Introducing trust and reputation into multi-agent systems can significantly improve the quality and efficiency of the systems. The computational trust and reputation also creates an environment of survival of the fittest to help agents recognize and eliminate malevolent agents in the virtual society. The research redefines the computational trust and analyzes its features from different aspects. A systematic model called Neural Trust Model for Multi-agent Systems is proposed to support trust learning, trust estimating, reputation generation, and reputation propagation. In this model, the research innovates the traditional Self Organizing Map (SOM) and creates a SOM based Trust Learning (STL) algorithm and SOM based Trust Estimation (STE) algorithm. The STL algorithm solves the problem of learning trust from agents' past interactions and the STE solve the problem of estimating the trustworthiness with the help of the previous patterns. The research also proposes a multi-agent reputation mechanism for generating and propagating the reputations. The mechanism exploits the patterns learned from STL algorithm and generates the reputation of the specific agent. Three propagation methods are also designed as part of the mechanism to guide path selection of the reputation. For evaluation, the research designs and implements a test bed to evaluate the model in a simulated electronic commerce scenario. The proposed model is compared with a traditional arithmetic based trust model and it is also compared to itself in situations where there is no reputation mechanism. The results state that the model can significantly improve the quality and efficacy of the test bed based scenario. Some design considerations and rationale behind the algorithms are also discussed based on the results.

AgentTMS: A MAS Trust Model based on Agent Social Relationship

2012 ◽  
Vol 7 (6) ◽  
Author(s):  
Min Peng ◽  
ZhengQuan Xu ◽  
ShaoMing Pan ◽  
Rui Li ◽  
Tengyue Mao
Keyword(s):  
Social Relationship ◽  
Trust Model ◽  
Model Based

Study on Quantum Trust Model Based on Node Trust Evaluation

2017 ◽  
Vol 26 (3) ◽  
pp. 608-613
Author(s):  
Shibin Zhang ◽  
Zhihai Xie ◽  
Yifen Yin ◽  
Yan Chang ◽  
Zhiwei Sheng ◽  
...  
Keyword(s):  
Trust Model ◽  
Trust Evaluation ◽  
Model Based

Trust Management Model based on Fuzzy Approach for Ubiquitous Computing

2020 ◽  
pp. 398-412
Author(s):  
Nalini A. Mhetre ◽  
Arvind V. Deshpande ◽  
Parikshit Narendra Mahalle
Keyword(s):  
Ubiquitous Computing ◽  
Trust Management ◽  
Future Internet ◽  
Trust Model ◽  
Model Based ◽  
Trust Relationships ◽  
Current State ◽  
Proposed Model ◽  
Security Properties ◽  
Trust Models

The current state of ubiquitous computing has been greatly influenced by emerging networking developments like Internet of Things (IoT), Future Internet etc. Adequate trust management is crucial to provide security. The entities involved in communication must be trusted for specific purposes depending on their role. Using trust model, devices can run trust computations and guide their behaviors. To this effect, a method is needed to evaluate the level of trust between devices. Trust models investigated so far discusses that devices face problems when communicating as transforming trust relationships from real to virtual world requires the negotiation of trust based on the security properties of devices. However, these models are developed in limited devices. This paper proposes a distributed trust model for device-to-device communication in ubiquitous computing. Mathematical model based on fuzzy rules to establish trust is presented. Fuzzy simulation of the model is presented to validate the findings. Simulation results show that proposed model calculates fuzzy trust values reliably.

scholarly journals Evaluating E-Commerce Trust Using Fuzzy Logic

2009 ◽  
pp. 272-285
Author(s):  
Farid Meziane
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Reasoning ◽  
Trust Model ◽  
Trust Evaluation ◽  
Human Relationships ◽  
Online Transactions ◽  
Proposed Model ◽  
Evaluation Models ◽  
Trust Models ◽  
Fuzzy Trust

Trust is widely recognized as an essential factor for the continual development of business to customer electronic commerce (B2C EC). Many trust models have been developed, however, most are subjective and do not take into account the vagueness and ambiguity of EC trust and the customers’ intuitions and experience when conducting online transactions. In this article, we develop a fuzzy trust model using fuzzy reasoning to evaluate EC trust. This trust model is based on the information customers expect to find on an EC Website and is shown to increase customers trust towards online merchants. We argue that fuzzy logic is suitable for trust evaluation as it takes into account the uncertainties within ecommerce data and like human relationships; it is often expressed by linguistics terms rather then numerical values. The evaluation of the proposed model will be illustrated using two case studies and a comparison with two evaluation models was conducted to emphasise the importance of using fuzzy logic.

Neural Trust Model

2017 ◽  
pp. 296-316
Author(s):  
Gehao Lu ◽  
Joan Lu
Keyword(s):  
Theoretical Foundation ◽  
Dynamic Structure ◽  
Trust Model ◽  
Static Structure ◽  
Specific Component ◽  
Learning Capability ◽  
Computational Trust ◽  
Trust And Reputation ◽  
Proposed Model ◽  
Definition Of

The problems found in the existing models push the researcher to look for a better solution for computational trust and computational reputation. According the problem exposed earlier, the newly proposed model should be a systematic model which supports both trust and reputation. The model should also take the learning capability for agents into consideration because agents cannot quickly adapt to the changes without learning. The model also needs to have the ability to make decisions according to its recognition of trust. Before actually building the model, it is necessary to analyze the concept of trust. Usually when people say trust they mean human trust, however, in this research trust refers to computational trust. How human trust is different from computational trust is a very interesting question. The answers to the question helped the researcher recover many features of computational trust and built a solid theoretical foundation for the proposed model. The definitions of trust in different disciplines such as economy, sociology and psychology will be compared. A possible definition of computational trust will be made and such trust from several different perspectives will be analyzed. The description of the model is important. As a whole, it is represented as a framework that defines components and component relationships. As the concrete components, the purposes and responsibilities of the specific component are explained. This is to illustrate the static structure of the model. The dynamic structure of the model is described as the process of executing the model.

scholarly journals A social commerce information propagation prediction model based on transformer

2021 ◽  
Vol 336 ◽  
pp. 05012
Author(s):  
Huibing Zhang ◽  
Jingrui Dai ◽  
Junfei He ◽  
Huacheng Zhang
Keyword(s):  
Prediction Model ◽  
Forecast Accuracy ◽  
Data Sets ◽  
Information Propagation ◽  
Social Commerce ◽  
Model Based ◽  
Proposed Model ◽  
Propagation Prediction ◽  
Fully Connected

Precise forecasts of the propagation patterns of social commerce information play a crucial part in precision marketing. Traditional forecast relies on machine learning diffusion models, in which the forecast accuracy is dependent on the quality of the designed features. Researchers using these models are required to have experience in this regard, but due to the complexity and variations of real-world social commerce information propagation, design of features for the prediction model turns out difficult and is likely to cause local or universal errors in the model. To address these problems, this study proposed an information propagation prediction model based on Transformer. First, the fully-connected neural network was employed to code the user nodes to low-dimension vectors; then, Transformer was employed to perform information of the user-node vectors; last, the output of the Transformer was uploaded to the output layer to forecast the next user node in information propagation. The model was tested on data sets obtained from Sina Weibo, and the test result shows that the proposed model outperformed baseline models in terms of the indicators of Acc@k and MRR.

A Robust Trust Model Based on Group for P2P Environments

2010 ◽  
Vol 439-440 ◽  
pp. 98-103
Author(s):  
Guang Hua Zhang ◽  
Zhen Guo Chen
Keyword(s):  
Trust Model ◽  
False Feedback ◽  
Initial Trust ◽  
Model Based ◽  
Proposed Model ◽  
Critical Issues ◽  
System Overhead ◽  
Trust Models ◽  
Indirect Trust ◽  
Trust Calculation

A robust trust model based on group for P2P environments-RobustGroup is put forward, which measures trust values based on the group relationship between requesting peers and serving peers. Trust’s aging time and peers’ similarity are introduced with weights for direct trust and indirect trust being dynamically adjusted in trust calculation against critical issues affecting trust evaluation such as false feedback, incentive and penalty, Whitewashing behavior, dynamic trust. Simultaneously service control policies are implemented on the basis of groups, and peers’ initial trust is determined by behavior in “silent time”. The simulation shows that the proposed model has advantages in successful requests and system overhead over the existing P2P trust models.

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