Multi-dimensional Trust Model Research Articles

A multi-dimensional trust model for misbehavior detection in vehicular ad hoc networks

Communication security is one of the focus issues of vehicular ad hoc networks (VANETs). Trust model is commonly used to identify malicious attackers or filter false messages in the network, which effectively guarantees secure communication between vehicles. Constrained by the distributed network architecture of VANETs, trust evaluation results may be biased due to uneven data distribution and lack of reference. By colluding with the partners, malicious vehicle launches imbalance attack and zigzag attack to bypass the detection of the trust model. To address these new intelligent attacks, we propose a multi-dimensional trust model (MDT) in this paper, which makes a combined evaluation of different trust attributes from vehicles. In MDT, data collection and trust calculation are deployed in vehicles and trust authority (TA), respectively. Since the model dynamically adjusts the corresponding weight coefficient according to the data fluctuations of each trust indicator by adopting entropy weight method and filters anomalous evaluation results by using the median absolute deviation (MAD) algorithm, it can effectively deal with complex and variable intelligent attacks. The legitimate identity of a vehicle will be revoked from the network by TA once its trust value is less than the threshold. We have conducted extensive experimental studies to evaluate the performance of MDT. The experimental results show that the MDT model can accurately and effectively calculate the global trust of vehicles and detect malicious attacks in the network under different scenarios and tests.

SLA-based Multi-dimensional Trust Model for Fog Computing Environments

SLA-based Multi-dimensional Trust Model for Fog Computing Environments

COMMTRUST

Abstract: We propose CommTrust for trust evaluation by mining feedback comments. Our main contributions include: 1) we propose a multidimensional trust model for computing reputation scores from user feedback comments; and 2) we propose an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining, and topic modeling. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the “all good reputation” issue and rank sellers effectively. To the best of our knowledge, our research is the first piece of work on trust evaluation by mining feedback comments.. An algorithm is proposed to mine feedback comments for dimension weights, ratings, which combine methods of topic modeling, natural language processing and opinion mining. This model has been experimenting with the dataset which includes various user level feedback comments that are obtained on various products. It also finds various multi-dimensional features and their ratings using Gibbs-sampling that generates various categories for feedback and assigns trust score for each dimension under each product level. Keywords: E-Commerce, Feedback mining, Trust score, Topic modeling, Reputation-based trust score

Commtrust: A Multi-Dimensional Trust Model for E-Commerce Applications

Commtrust: A Multi-Dimensional Trust Model for E-Commerce Applications

A Multi-Dimensional Trust Model for Processing Big Data Over Competing Clouds

4Cloud computing has emerged as a powerful paradigm for delivering data-intensive services over the Internet. Cloud computing has enabled the implementation and success of big data, a recent phenomenon handling huge data being generated from different sources. Competing clouds have made it challenging to select a cloud provider that guarantees quality of cloud service (QoCS). Also, cloud providers’ claims of guaranteeing QoCS are exaggerated for marketing purposes; hence, they cannot often be trusted. Therefore, a comprehensive trust model is necessary to evaluate the QoCS prior to making any selection decision. In this paper, we propose a multi-dimensional trust model for big data workflow processing over different clouds. It evaluates the trustworthiness of cloud providers based on: the most up-to-date cloud resource capabilities, the reputation evidence measured by neighboring users, and a recorded personal history of experiences with the cloud provider. The ultimate goal is to ensure an efficient selection of trustworthiness cloud provider who eventually will guarantee high QoCS and fulfills key big data workflow requirements. Various experiments were conducted to validate our proposed model. The results show that our model captures the different components of trust, ensures high QoCS, and effectively adapts to the dynamic nature of the cloud.

Robust recommendation method based on suspicious users measurement and multidimensional trust

The existing collaborative recommendation algorithms have poor robustness against shilling attacks. To address this problem, in this paper we propose a robust recommendation method based on suspicious users measurement and multidimensional trust. Firstly, we establish the relevance vector machine classifier according to the user profile features to identify and measure the suspicious users in the user rating database. Secondly, we mine the implicit trust relation among users based on the user-item rating data, and construct a reliable multidimensional trust model by integrating the user suspicion information. Finally, we combine the reliable multidimensional trust model, the neighbor model and matrix factorization model to devise a robust recommendation algorithm. The experimental results on the MovieLens dataset show that the proposed method outperforms the existing methods in terms of both recommendation accuracy and robustness.

Survey on CommTrust: Multi-DimensionalTrust Using Mining E-Commerce FeedbackComments

The Reputation based trust models are very admirable in ecommerce applications .Feedback ratings are gathered together for computing sellers’ reputation trust scores. A CommTrust system is proposed where the observation made by buyers are mostly used to express opinions about the product in free text feedback review. These feedback review comments are mined. In CommTrust 1) a multidimensional trust model is proposed for computing reputation scores from user feedback comments, 2) for mining feedback comments which are used for weights and ratings of dimension an algorithm is proposed; natural language processing’s combining techniques, opinion mining, and topic modeling. The CommTrust proved to be very effective after testing it on various websites like Amazon and eBay.

Reputation-based multi-dimensional trust model in cloud manufacturing service platform

In previous researches of cloud manufacturing services, trust evaluation models were not flexible enough and subjective preferences of agents were not reflected either. For this issue, a five-dimensional trust evaluation system is built, and a reputation-based trust evaluation algorithm (RBTE) is also proposed. The direct trust rank (DT), friend reputation (RF) and platform reputation (RP) of the agent could be integrated into the trust rank (TR) with the weight vector through this algorithm. Combined with similarities of trust evaluation process, a trust network building approach is put forward in this research. The trust rank update algorithm also achieved dynamic rewards or punishments over time. Experimental results has shown good executing efficiency, and reflected the subjective preference of the transaction agent to a certain extent.

Recommendation system for sharing economy based on multidimensional trust model

The recommendation system are widely adopted in today's mainstream online sharing services, providing useful prediction of user's rating or user's preferences of sharing items (such as products, movies, books, and news articles). A key challenge of recommendation systems in sharing economy is to employ prediction algorithms to estimate the matching items with considering their interests and needs. The environment-context has been recognized as an important factor to consider in personalized recommender systems. Since dynamic information in environment-context describes the situation of items and users, the information affects the user's decision process essentially to apply in recommender systems. However, most model-based collaborative filtering approaches such as Matrix Factorization do not provide an easy way of integrating context information into the model. In this paper, we introduce a Multidimensional Trust model based on Tensor Factorization. The generalization of Matrix Factorization allows for a flexible and generic integration of contextual information. According to the different types of context, the Multidimensional Trust model considers the additional dimensions for the representation of the data as a tensor. This is achieved by going through the collecting user's behavior based on rating analysis and identification of users' historical activity and viewing patterns. The benefits behavior solutions, which use the handle intelligently to meet the users' needs, are the focus of this paper.

Attitudes and Trust of 6-year-old Children to Unfamiliar Adults: The Pilot Study

A multi-dimensional trust model is presented, which includes two dimensions: (a) levels of trust (interpersonal, microgroup, and group), and (b) types of trust (information-influentive, confidentially-protective, and activity-coping). The sample included 51 six-year-old Russian children (27 boys, 24 girls). A projective Test of Children's Trust To Unfamiliar Adults was applied. The study examined the components (cognitive, affective, and behavioral) of the interpersonal confidentially-protective children's trust to the unfamiliar adults. It showed that a lot of children have the attitude to the contact with the unfamiliar adults. 6-year-olds lacked the differentiated system of conceptions about the qualities of the unfamiliar adults and they attributed general and formal characteristics to them. Most of the children perceived the unfamiliar adults positively. The system of emotional attitudes towards adult strangers of some children appeared not to have been developed. Children more often demonstrated the behavioral distrust to the unfamiliar adults than trust to them.

CommTrust: Computing Multi-Dimensional Trust by Mining E-Commerce Feedback Comments

Reputation-based trust models are widely used in e-commerce applications, and feedback ratings are aggregated to compute sellers' reputation trust scores. The “all good reputation” problem, however, is prevalent in current reputation systems-reputation scores are universally high for sellers and it is difficult for potential buyers to select trustworthy sellers. In this paper, based on the observation that buyers often express opinions openly in free text feedback comments, we propose CommTrust for trust evaluation by mining feedback comments. Our main contributions include: 1) we propose a multidimensional trust model for computing reputation scores from user feedback comments; and 2) we propose an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining, and topic modeling. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the “all good reputation” issue and rank sellers effectively. To the best of our knowledge, our research is the first piece of work on trust evaluation by mining feedback comments.

A Robust Collaborative Recommendation Algorithm Incorporating Trustworthy Neighborhood Model

The conventional collaborative recommendation algorithms are quite vulnerable to user profile injection attacks. To solve this problem, in this paper we propose a robust collaborative recommendation algorithm incorporating trustworthy neighborhood model. Firstly, we present a method to calculate the users’ degree of suspicion based on the user-item ratings data using the theory of entropy and the idea of density-based local outlier factor. Based on it, we measure the user’s trust attributes from different angles by introducing the source credibility theory and propose a multidimensional trust model incorporating users’ degree of suspicion. Then we propose a trustworthy neighborhood model by combining the baseline estimate approach with the multidimensional trust model. Finally, we devise a robust collaborative recommendation algorithm to provide more accurate recommendation for the target user by integrating the M-estimator based matrix factorization approach and the trustworthy neighborhood model. Experimental results on the MovieLens dataset show that the proposed algorithm has better robustness in comparison with the existing collaborative recommendation algorithms.

Multi-Dimensional Trust: how beginning principals build trust with their staff during leader succession

As part of principal succession, new school leaders must take action to solidify their position as the school’s legitimate lead authority while at the same time, develop and utilize interactive mechanisms designed to nurture staff relations and engender teacher support and confidence in their leadership. For beginning principals, this process highlights the critical importance of understanding the influences and expectations of the leadership context and the need for building and maintaining trust. This paper explores how beginning principals attend to multidimensional criteria when building trust with their staff as part of early-phase school leadership during principal succession. New principals identified using actionable principles and behaviours related to knowledge/skills, interaction, and communication as a means to engender trust with their colleagues. Findings were combined with concepts associated with educational trust in extant research to create the conceptualization of Three-Dimensional Trust. The multidimensional trust model includes three different dimensions of criteria for each of two areas of ability—task ability and interpersonal ability.

Multi-criteria decision making based on trust and reputation in supply chain

Decision making is a core problem in Supply Chains. A large number of studies in literature have reported various decision making techniques based on customers' requirements. Taking into account high risk transactions in virtual Supply Chain market, trust is a very critical element and should be treated as an important reference when customers try to select proper suppliers. Recently, a great effort has been carried out to develop decision making based on trust and reputation. However, these research works still stay on the stage of theoretical research. This paper presents and implements a multi-criteria decision making approach based on trust and reputation in Supply Chain. Firstly, this paper defines general trust indicators in real Supply Chain settings, and designs a multi-dimensional trust and reputation model. This paper also introduces K-mean clustering algorithm to remove unfair rating scores. Then, based on this trust and reputation model, we propose a multi-criteria decision making approach based on variable weights and satisfaction principle. In order to validate the performance of this approach, we simulate a practical Supply Chain setting with multi-agents platform. The simulation experiments demonstrate that the proposed trust and reputation model can effectively filter unfair ratings from those customers who did lie and the proposed multi-criteria decision making method can help customers make right decisions.

A Robust Collaborative Filtering Recommendation Algorithm Based on Multidimensional Trust Model

Collaborative filtering is one of the widely used technologies in the e-commerce recommender systems. It can predict the interests of a user based on the rating information of many other users. But the traditional collaborative filtering recommendation algorithm has the problems such as lower recommendation precision and weaker robustness. To solve these problems, in this paper we present a robust collaborative filtering recommendation algorithm based on multidimensional trust model. Firstly, according to the rating information of users, a multidimensional trust model is proposed. It measures the credibility of user’s ratings from the following three aspects: the reliability of item recommendation, the rating similarity and the user’s trustworthiness. Secondly, the computational model of trust and the traditional collaborative filtering approach are combined to select the reliable neighbor set and generate recommendation for the target user. Finally, the performances of the novel algorithm with others are compared from both sides of recommendation precision and robustness using MovieLens dataset. Compared with the existing algorithms, the proposed algorithm not only improves the quality of neighbor selection and the recommendation precision, but also has better robustness.

A dynamic multi-dimensional trust evaluation model to enhance security of cloud computing environments

High security of cloud computing is one of the most challenges to be addressed before the novel pas-as-you-go business paradigm is widely applied over the internet. Trust brings a novel means to improve the security of cloud computing platforms. However, there are few systematic works on trust in cloud systems. In this paper, the definition of trust in cloud systems is given and the properties of trust are analysed by referring to the fruits from social science. Based on the properties and semantics of trust, a dynamic multi-dimensional trust model named DMTC is proposed, which includes a time-variant comprehensive evaluation multi-dimensional method for expressing direct trust and a space-variant evaluation multi-dimensional method for calculating recommendation trust. To compute trust in cloud systems, an algorithm based on the DMTC model is given. Experiment results show that the proposed model can dynamically and effectively construct the trust relationship in cloud computing environment.

Online Consumer Trust

Consumer trust is widely heralded as an essential element for the success of electronic commerce, yet the concept itself is still clouded in confusion by the numerous definitions and points of view held across multiple disciplines. It is the aim of this paper to synthesise the viewpoints from across the disciplines and bring them together in a multi-dimensional trust model. It is hoped that in doing so, these broad assortment of views will highlight the true underlying nature of trust in the online environment. From these multiple disciplines, three dimensions of trust emerged: dispositional trust, institutional trust and interpersonal trust, each bringing its own influencing factors into the overall intention to trust. From this model emerged the notion that the consumer as an individual is central to the understanding of trust, and in turn that the individual’s personality and culture form the foundation for the development of trust.