Trust Building and Managing in Service-oriented Environment

Services are ubiquitous. In daily life, we can find service provisioning everywhere such as online shopping, online storage, doctor, hotel, lawyer, restaurant, etc. With the development of Web 2.0 technology, a huge amount of information about services has become available on the Internet. For instance, on a review website people can discuss which restaurant serves the best Chinese food; in a blog, an author posts an article about the experience of visiting a doctor. The abundance of services and the overload of service information online, result in two main problems. The first problem is service selection; the second one is the overload of consumer-driven information which refers to information such as reviews, articles, assessments, and discussions generated by service consumers. The concept of trust is proposed to solve the two problems. The computational concept of trust is defined as a subjective probability, which makes a prediction of the occurrence of an event such as a good service provisioning. Software used for building and managing trust data related to service offerings, is called Trust Management System (TMS). The first topic is trust model. A trust model is the computing kernel of a TMS that calculates the trust value of a service. Another significant topic regarding trust management for services is the robustness of a TMS. Robustness of a TMS refers to the ability of a TMS to cope with inaccuracy (deliberate or accidental) in the consumer-provided information used for computing trust. There are many trust models that have been proposed. I do not know of any survey analyzing and comparing different trust models with respect to trust in services. In this thesis, 40 trust models are compared from both a theoretical and a practical perspective, using criteria such as application context, information representation, properties of trust evaluation, and robustness of system. In addition, a trust model framework for service provisioning is proposed. This framework is considered a meta-model covering all existing trust models. A concrete trust model can be derived by instantiating the meta-model. In the thesis, four concrete services which cover both quantitative and qualitative services are studied. A quantitative service refers to a service the quality of which can be measured objectively. For a qualitative service there is no general agreed-upon objective measure for service quality. The first case study is about Online File Storage Service (OFSS) which is categorized as a quantitative service. The trust model, R-Rep, for a OFSS is proposed. In order to mitigate manipulation, a statistics based detection mechanism, named Baseline Sampling (BS), is introduced. In addition, when social network information among users is available, Clique Identification (CI) is used to detect manipulative groups. One e-commerce website, Taobao.com, and two review websites, TripAdvisor.com and Dianping.com, are chosen as case studies for trust building and managing in the context of qualitative service. For each case, specific trust models which consider intrinsic robustness enhancement by designing special weight functions are proposed. Meanwhile, machine learning-based extrinsic robustness enhancement is applied. Three types of machine learning approaches, clustering, classification and Annotation-Auxiliary Clustering (AAClust), are applied to identify manipulative behavior

Trust beyond reputation: A computational trust model based on stereotypes

Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger's actions in absence of the knowledge of such behavioral history, we often use our "instinct"- essentially stereotypes developed from our past interactions with other "similar" persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger's profile. Since stereotypes are formed locally, recommendations stem from the trustor's own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information

An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.Comment: 8 pages, 5 figure

Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

The Impact of Trust on Acceptance of Online Banking

Major benefits of Online Banking include for banks cost savings, and for customers convenience. Nevertheless, many people perceive Internet banking as risky. This paper introduces a tentative conceptual framework. Trust will be integrated into the Technology Acceptance Model – TAM - (Davis, 1989). Recent research showed that Trust has a striking influence on user willingness to engage in online exchanges of money and personal sensitive information. Detailed literature about Online Banking and Trust is provided. TAM is discussed in depth; external variables that are suitable for the Online Banking context is suggested. In addition the theoretical justification for the conceptual framework integration is discussed. Finally managerial implications and recommendations for Online Banking acceptance are suggested

Trust Model for Optimized Cloud Services

Cloud computing with its inherent advantages draws attention for business critical applications, but concurrently expects high level of trust in cloud service providers. Reputation-based trust is emerging as a good choice to model trust of cloud service providers based on available evidence. Many existing reputation based systems either ignore or give less importance to uncertainty linked with the evidence. In this paper, we propose an uncertainty model and define our approach to compute opinion for cloud service providers. Using subjective logic operators along with the computed opinion values, we propose mechanisms to calculate the reputation of cloud service providers. We evaluate and compare our proposed model with existing reputation models