Trust in information sources: seeking information from people,documents, and virtual agents

The notion of trust has been virtually absent from most work on how people assess and choose their information sources. Based on two empirical cases this study shows that software engineers and users of e-commerce websites devote a lot of attention to considerations about the trustworthiness of their sources, which include people, documents, and virtual agents. In the project-based software engineering environment trust tends to be a collaborative issue and the studied software engineers normally know their sources first-hand or have them recommended by colleagues. Outside this network people are cautious and alert to even feeble cues about source trustworthiness. For example, users of e-commerce websites—generally perceived as single-user environments—react rather strongly to the visual appearance of virtual agents, though this is clearly a surface attribute. Across the two cases people need access to their sources in ways that enable them to assess source trustworthiness, access alone is not enough.     

Trust structures

A general formal model for trust in dynamic networks is presented. The model is based on the trust structures of Carbone, Nielsen and Sassone: a domain theoretic generalisation of Weeks’ framework for credential based trust management systems, e.g., KeyNote and SPKI. Collections of mutually referring trust policies (so-called “webs” of trust) are given a precise meaning in terms of an abstract domain-theoretic semantics. A complementary concrete operational semantics is provided using the well-known I/O-automaton model. The operational semantics is proved to adhere to the abstract semantics, effectively providing a distributed algorithm allowing principals to compute the meaning of a “web” of trust policies. Several techniques allowing sound and efficient distributed approximation of the abstract semantics are presented and proved correct. BRICS: Basic Research in Computer Science (www.brics.dk) funded by the Panish National Research Foundation.     

Evaluating trustworthiness from past performances: interval-based approaches

In many multi-agent systems, especially in the field of e-commerce, the users have to decide whether they sufficiently trust an agent to achieve a certain goal. To help users to make such decisions, an increasing number of trust systems have been developed. By trust system, we mean a system that gathers information about an agent and evaluates its trustworthiness on the basis of this information. The aim of the present paper is to develop, and analyze from an axiomatic point of view, new trust systems based on intervals. More precisely, we assume that a set of grades describing the past performances of an agent is given. Then, the goal is to construct an interval that summarizes these grades. In our opinion, such an interval gives a good account of the trustworthiness of the agent. In addition, this kind of representation format overcomes certain limitations (at a certain cost) of the approaches that represent trustworthiness by a single number. We establish seven axioms that should be satisfied by a summarizing method. Next, we develop two new methods. The first one is based on the idea that certain concentrations of grades are strong enough to pull the bounds of the summarizing interval towards themselves. The second one represents data in the setting of possibility theory, and then computes lower and upper expected values. Finally, we check that our methods satisfy the axioms introduced before, which provide theoretical justifications for them.     

Engineering self-organizing referral networks for trustworthy service selection

Developing, maintaining, and disseminating trust in open, dynamic environments is crucial. We propose self-organizing referral networks as a means for establishing trust in such environments. A referral network consists of autonomous agents that model others in terms of their trustworthiness and disseminate information on others' trustworthiness. An agent may request a service from another; a requested agent may provide the requested service or give a referral to someone else. Possibly with its user's help, each agent can judge the quality of service obtained. Importantly, the agents autonomously and adaptively decide with whom to interact and choose what referrals to issue, if any. The choices of the agents lead to the evolution of the referral network, whereby the agents move closer to those that they trust. This paper studies the guidelines for engineering self-organizing referral networks. To do so, it investigates properties of referral networks via simulation. By controlling the actions of the agents appropriately, different referral networks can be generated. This paper first shows how the exchange of referrals affects service selection. It identifies interesting network topologies and shows under which conditions these topologies emerge. Based on the link structure of the network, some agents can be identified as authorities. Finally, the paper shows how and when such authorities emerge. The observations of these simulations are then formulated into design recommendations that can be used to develop robust, self-organizing referral networks.     

Applying virtual reality for trust-building e-commerce environments

The application of virtual reality in e-commerce has enormous potential for transforming online shopping into a real-world equivalent. However, the growing research interest focuses on virtual reality technology adoption for the development of e-commerce environments without addressing social and behavioral facets of online shopping such as trust. At the same time, trust is a critical success factor for e-commerce and remains an open issue as to how it can be accomplished within an online store. This paper shows that the use of virtual reality for online shopping environments offers an advanced customer experience compared to conventional web stores and enables the formation of customer trust. The paper presents a prototype virtual shopping mall environment, designed on principles derived by an empirically tested model for building trust in e-commerce. The environment is evaluated with an empirical study providing evidence and explaining that a virtual reality shopping environment would be preferred by customers over a conventional web store and would facilitate the assessment of the e-vendor’s trustworthiness.     

GenTrust: A genetic trust management model for peer-to-peer systems

In recent years, peer-to-peer systems have attracted significant interest by offering diverse and easily accessible sharing environments to users. However, this flexibility of P2P systems introduces security vulnerabilities. Peers often interact with unknown or unfamiliar peers and become vulnerable to a wide variety of attacks. Therefore, having a robust trust management model is critical for such open environments in order to exclude unreliable peers from the system. In this study, a new trust model for peer-to-peer networks called GenTrust is proposed. GenTrust has evolved by using genetic programming. In this model, a peer calculates the trustworthiness of another peer based on the features extracted from past interactions and the recommendations. Since the proposed model does not rely on any central authority or global trust values, it suits the decentralized nature of P2P networks. Moreover, the experimental results show that the model is very effective against various attackers, namely individual, collaborative, and pseudospoofing attackers. An analysis on features is also carried out in order to explore their effects on the results. This is the first study which investigates the use of genetic programming on trust management.     

Trustworthiness in electronic commerce: the role of privacy, security, and site attributes

While the growth of business-to-consumer electronic commerce seems phenomenal in recent years, several studies suggest that a large number of individuals using the Internet have serious privacy concerns, and that winning public trust is the primary hurdle to continued growth in e-commerce. This research investigated the relative importance, when purchasing goods and services over the Web, of four common trust indices (i.e. (1) third party privacy seals, (2) privacy statements, (3) third party security seals, and (4) security features). The results indicate consumers valued security features significantly more than the three other trust indices. We also investigated the relationship between these trust indices and the consumer's perceptions of a marketer's trustworthiness. The findings indicate that consumers' ratings of trustworthiness of Web merchants did not parallel experts' evaluation of sites' use of the trust indices. This study also examined the extent to which consumers are willing to provide private information to electronic and land merchants. The results revealed that when making the decision to provide private information, consumers rely on their perceptions of trustworthiness irrespective of whether the merchant is electronic only or land and electronic. Finally, we investigated the relative importance of three types of Web attributes: security, privacy and pleasure features (convenience, ease of use, cosmetics). Privacy and security features were of lesser importance than pleasure features when considering consumers' intention to purchase. A discussion of the implications of these results and an agenda for future research are provided.     

Trust fraud: A crucial challenge for China’s e-commerce market

Currently, China’s e-commerce market is growing at an unprecedented pace, however, it is faced with many challenges, among which the trust fraud problem is the biggest issue. In this article, we use Taobao as an example and conduct a thorough investigation of the trust fraud phenomenon in China’s e-commerce market. We present the development history of trust fraud, summarize its unique characteristics, and explore the reasons why so many sellers commit fraud. We further propose a dynamic time decay trust model that aims to deter trust fraud by raising its cost and promote the growth of small and medium-sized sellers. The model utilizes detailed seller ratings as the data source, and incorporates a transaction amount weight, a time decay coefficient, and three trust factors in the calculation of trust. We test the model on real transaction data from Taobao, and the experimental results verify its effectiveness. Our proposed trust model yields a practical approach to online trust management not only in the Taobao market but also for other e-commerce platforms.     

On the role of trust in collaborative Web search

Recommender systems combine ideas from information retrieval, user modelling, and artificial intelligence to focus on the provision of more intelligent and proactive information services. As such, recommender systems play an important role when it comes to assisting the user during both routine and specialised information retrieval tasks. Like any good assistant it is important that users can trust in the ability of a recommender system to respond with timely and relevant suggestions. In this paper, we will look at a collaborative recommendation system operating in the domain of Web search. We will show how explicit models of trust can help to inform more reliable recommendations that translate into more relevant search results. Moreover, we demonstrate how the availability of this trust-model facilitates important interface enhancements that provide a means to declare the provenance of result recommendations in a way that will allow searchers to evaluate their likely relevance based on the reputation and trustworthiness of the recommendation partners behind these suggestions.     

Trust in consumer-to-consumer electronic commerce

We developed a model of consumer-to-consumer (C2C) e-commerce trust and tested it. We expected that two influences: internal (natural propensity to trust [NPT] and perception of web site quality [PWSQ]) and external (other's trust of buyers/sellers [OTBS] and third party recognition [TPR]) would affect an individual's trust in C2C e-commerce. However contrary to studies of other types of e-commerce, support was only found for PWSQ and TPR; we therefore discussed possible reasons for this contradiction. Suggestions are made of ways to help e-commerce site developers provide a trustworthy atmosphere and identify trustworthy consumers.     

分布式多信任域信任管理技术研究综述

随着Internet技术的迅速发展和广泛应用,出现了基于Internet虚拟社会,如P2P、网格、无线网络、多代理网络、无线传感器网络等,从而引起了信任概念从人类交互世界向虚拟交互世界的迁移。近年来研究者针对虚拟交互世界提出了各种信任管理模型,这些模型的多样性使得在实际应用中会产生多种信任域。一些文献提出了如何实现多信任域中代理间的可信交互。分析了多信任域的相关概念及其产生的原因,对选取新的典型的多信任域信任管理模型及其使用的各种方法进行了评述;分析了目前研究中存在的问题,并展望了未来的发展方向。研究表明,多信任域中代理间的可信交互是目前信任管理中的难点问题。     

Improving trust modeling through the limit of advisor network size and use of referrals

This paper explores potential improvements to the trust modeling of agents in multi-agent systems when a social network of advisors is employed as part of the trust modeling, and in particular, examines means of optimizing the number of advisors that should be maintained in the social network. We propose three such improvements, two directly relating to the limit of advisor network size by either setting a maximum size for a buyer’s advisor network or setting a minimum trustworthiness threshold for agents to be accepted into that advisor network, and a third which uses an advisor referral system in combination with one of the first two network-limiting techniques. We provide experimental results in defence of our approach for two distinct trust modeling systems, and show how these optimizations can improve, sometimes significantly, the accuracy of different trust models (in the context of electronic marketplaces). We believe that the proposed techniques will be very useful for trust researchers seeking to improve the accuracy of their own trust models by setting the size and composition of advisor networks.     

Learning to trust in the competence and commitment of agents

For agents to collaborate in open multi-agent systems, each agent must trust in the other agents’ ability to complete tasks and willingness to cooperate. Agents need to decide between cooperative and opportunistic behavior based on their assessment of another agents’ trustworthiness. In particular, an agent can have two beliefs about a potential partner that tend to indicate trustworthiness: that the partner is competent and that the partner expects to engage in future interactions. This paper explores an approach that models competence as an agent’s probability of successfully performing an action, and models belief in future interactions as a discount factor. We evaluate the underlying decision framework’s performance given accurate knowledge of the model’s parameters in an evolutionary game setting. We then introduce a game-theoretic framework in which an agent can learn a model of another agent online, using the Harsanyi transformation. The learning agents evaluate a set of competing hypotheses about another agent during the simulated play of an indefinitely repeated game. The Harsanyi strategy is shown to demonstrate robust and successful online play against a variety of static, classic, and learning strategies in a variable-payoff Iterated Prisoner’s Dilemma setting.     

A multi-dimensional trust evaluation model for large-scale P2P computing

In large-scale peer-to-peer (P2P) computing, trust management technology has become a key requirement for network security. However, trust is one of the most complex concepts in network communities. It involves many factors, such as, assumptions, expectations, behaviors, risks, and so on. As a result, trustworthiness has multi-dimensional properties. In this paper, an innovative trust model is proposed for large-scale P2P computing, in which multiple factors are incorporated to reflect the complexity of trust. More importantly, the properties (weights) of these factors are dynamically assigned by weighted moving average and ordered weighted averaging (WMA-OWA) combination algorithms. This model surpasses the limitations of existing approaches, wherein weights are assigned subjectively. The simulation results show that, compared with the existing approaches, the proposed model provides greater accuracy and a more detailed analysis in trust evaluation.     

TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources

In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. There is therefore a need to develop a model of trust and reputation that will ensure good interactions among software agents in large scale open systems. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent’s trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents, and when there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate.     

H-Trust: A Group Trust Management System for Peer-to-Peer Desktop Grid

Peer-to-Peer Desktop Grid (P2PDG) has emerged as a pervasive cyber-infrastructure tackling many large-scale applications with high impacts. As a burgeoning research area, P2PDG can support numerous applications, including scientific computing, file sharing, web services, and virtual organization for collaborative activities and projects. To handle trustworthiness issues of these services, trust and reputation schemes are proposed to establish trust among peers in P2PDG. In this paper, we propose a robust group trust management system, called H-Trust, inspired by the H-index aggregation technique. Leveraging the robustness of the H-index algorithm under incomplete and uncertain circumstances, H-Trust offers a robust personalized reputation evaluation mechanism for both individual and group trusts with minimal communication and computation overheads. We present the H-Trust scheme in five phases, including trust recording, local trust evaluation, trust query phase, spatial-temporal update phase, and group reputation evaluation phases. The rationale for its design, the analysis of the algorithm are further investigated. To validate the performance of H-Trust scheme, we designed the H-Trust simulator HTrust-Sim to conduct multi-agent-based simulations. Simulation results demonstrate that H-Trust is robust and can identify and isolate malicious peers in large scale systems even when a large portion of peers are malicious.     

The dynamics of trust in B2C e-commerce: a research model and agenda

Trust is a key factor that determines the success of Business to Consumer (B2C) e-commerce transactions. Research has identified several critical factors that influence trust. These have been incorporated into the design of e-commerce web portals; however a vast majority of potential users are still wary of online business transactions. This points to the need for the development of more sophisticated and comprehensive models that can provide additional insights into the true role played by trust in the adoption of e-commerce portals. Here we do so by focusing on trust as it pertains to B2C e-commerce transactions. Pertinent issues addressed are: (1) Is trust the critical factor that makes all the difference between users accepting or rejecting a B2C portal? (2) Are there factors that could play a more critical role in user acceptance/rejection of such portals? (3) If so, what is the relevance of trust relative to these factors? The research model and agenda introduced here enable researchers to view trust in its proper perspective as part of the bigger picture of technology acceptance.     

A simulation testbed for analyzing trust and reputation mechanisms in unreliable online markets

Modern online markets are becoming extremely dynamic, indirectly dictating the need for (semi-) autonomous approaches for constant monitoring and immediate action in order to satisfy one’s needs/preferences. In such open and versatile environments, software agents may be considered as a suitable metaphor for dealing with the increasing complexity of the problem. Additionally, trust and reputation have been recognized as key issues in online markets and many researchers have, in different perspectives, surveyed the related notions, mechanisms and models. Within the context of this work we present an adaptable, multivariate agent testbed for the simulation of open online markets and the study of various factors affecting the quality of the service consumed. This testbed, which we call Euphemus, is highly parameterized and can be easily customized to suit a particular application domain. It allows for building various market scenarios and analyzing interesting properties of e-commerce environments from a trust perspective. The architecture of Euphemus is presented and a number of well-known trust and reputation models are built with Euphemus, in order to show how the testbed can be used to apply and adapt models. Extensive experimentation has been performed in order to show how models behave in unreliable online markets, results are discussed and interesting conclusions are drawn.