DART: A DISTRIBUTED ANALYSIS OF REPUTATION AND TRUST FRAMEWORK

Artificial societies—distributed systems of autonomous agents—are becoming increasingly important in open distributed environments, especially in e‐commerce. Agents require trust and reputation concepts to identify communities of agents with which to interact reliably. We have noted in real environments that adversaries tend to focus on exploitation of the trust and reputation model. These vulnerabilities reinforce the need for new evaluation criteria for trust and reputation models called exploitation resistance which reflects the ability of a trust model to be unaffected by agents who try to manipulate the trust model. To examine whether a given trust and reputation model is exploitation‐resistant, the researchers require a flexible, easy‐to‐use, and general framework. This framework should provide the facility to specify heterogeneous agents with different trust models and behaviors. This paper introduces a Distributed Analysis of Reputation and Trust (DART) framework. The environment of DART is decentralized and game‐theoretic. Not only is the proposed environment model compatible with the characteristics of open distributed systems, but it also allows agents to have different types of interactions in this environment model. Besides direct, witness, and introduction interactions, agents in our environment model can have a type of interaction called a reporting interaction, which represents a decentralized reporting mechanism in distributed environments. The proposed environment model provides various metrics at both micro and macro levels for analyzing the implemented trust and reputation models. Using DART, researchers have empirically demonstrated the vulnerability of well‐known trust models against both individual and group attacks.     

Computational trust and reputation models for open multi-agent systems: a review

In open environments, agents depend on reputation and trust mechanisms to evaluate the behavior of potential partners. The scientific research in this field has considerably increased, and in fact, reputation and trust mechanisms have been already considered a key elements in the design of multi-agent systems. In this paper we provide a survey that, far from being exhaustive, intends to show the most representative models that currently exist in the literature. For this enterprise we consider several dimensions of analysis that appeared in three existing surveys, and provide new dimensions that can be complementary to the existing ones and that have not been treated directly. Moreover, besides showing the original classification that each one of the surveys provide, we also classify models that where not taken into account by the original surveys. The paper illustrates the proliferation in the past few years of models that follow a more cognitive approach, in which trust and reputation representation as mental attitudes is as important as the final values of trust and reputation. Furthermore, we provide an objective definition of trust, based on Castelfranchi’s idea that trust implies a decision to rely on someone.     

A Framework for Trust and Reputation in Grid Environments

This paper examines the role of trust and reputation in Grid environments. As Grid technology becomes standardized and stable, business models are invented and increasingly applied, and economic implications can be observed. Asymmetrically distributed information may allow for opportunistic behavior of service providers or users who opportunistically exploit the information gap between providers and consumers on the quality of services. The paper takes up these economic issues by proposing a reputation-based conceptual framework for enabling future open Grid markets, to recommend the most promising Grid architecture and a corresponding reputation approach in a particular case.     

Fuzzy computational models for trust and reputation systems

In the recent past, a considerable research has been devoted to trust and reputation mechanisms to simplify complex transactions for open environments in social networking, e-commerce, and recommender systems (RS). In real life, we come to know about others through our social circle according to their reputation which is a public view. However, it is not always adequate to depend solely on the public view and therefore a trust measure is required to give a personalized view of the future encounters with a specific partner. In this paper, we propose fuzzy computational models for both trust and reputation concepts. Reciprocity and experience are used for trust modeling while the proposed reputation model is a fuzzy extension of beta reputation model. A two-level filtering methodology is proposed to benefit to a large extent from both the concepts separately. In order to justify the proposed models, we compared them with the existing reputation models for movie RS. The experimental results show that the incorporation of trust and reputation concepts into RS indeed improves the recommendation accuracy and establish that our models are better than beta and the popular eBay reputation models.     

Security threats scenarios in trust and reputation models for distributed systems

Trust and reputation management over distributed systems has been proposed in the last few years as a novel and accurate way of dealing with some security deficiencies which are inherent to those environments. Thus, many models and theories have been developed in order to effective and accurately manage trust and reputation in those communities. Nevertheless, very few of them take into consideration all the possible security threats that can compromise the system. In this paper, we present some of the most important and critical security threats that could be applied in a trust and reputation scheme. We will describe and analyze each of those threats and propose some recommendations to face them when developing a new trust and reputation mechanism. We will also study how some trust and reputation models solve them. This work expects to be a reference guide when designing secure trust and reputation models.     

TREET: the Trust and Reputation Experimentation and Evaluation Testbed

To date, trust and reputation systems have often been evaluated using methods of their designers’ own devising. Recently, we demonstrated that a number of noteworthy trust and reputation systems could be readily defeated, revealing limitations in their original evaluations. Efforts in the trust and reputation community to develop a testbed have yielded a successful competition platform, ART. This testbed, however, is less suited to general experimentation and evaluation of individual trust and reputation technologies. In this paper, we present TREET, an experimentation and evaluation testbed based directly on that used in our investigations into security vulnerabilities in trust and reputation systems for marketplaces. We demonstrate the advantages of TREET, towards the development of more thorough, objective evaluations of trust and reputation systems.     

DEVISING A TRUST MODEL FOR MULTI-AGENT INTERACTIONS USING CONFIDENCE AND REPUTATION

In open environments in which autonomous agents can break contracts, computational models of trust have an important role to play in determining who to interact with and how interactions unfold. To this end, we develop such a trust model, based on confidence and reputation, and show how it can be concretely applied, using fuzzy sets, to guide agents in evaluating past interactions and in establishing new contracts with one another.     

An extension of a fuzzy reputation agent trust model (AFRAS) in the ART testbed

With the introduction of web services, users require an automated way of determining their reliability and even their matching to personal and subjective preferences. Therefore, trust modelling of web services, managed in an autonomous way by intelligent agents, is a challenging and relevant issue. Due to the dynamic and distributed nature of web services, recommendations of web services from third parties may also play an important role to build and update automated trust models. In this context, the agent reputation and trust (ART) testbed has been used to compare trust models in three international competitions. The testbed runs locally and defines an ART appraisal domain with a simulation engine, although the trust models may be applied to any kind of automated and remote services, such as web services. Our previous works proposed an already-published trust model called AFRAS that used fuzzy sets to represent reputation of service providers and of recommenders of such services. In this paper we describe the extension required in the trust model to participate in these competitions. The extension consists of a trust strategy that applies the AFRAS trust model to the ART testbed concepts and protocols. An implementation of this extension of AFRAS trust model has participated in the (Spanish and International) 2006 ART competitions. Using this ART platform and some of the agents who participated, we executed a set of ART games to evaluate the relevance of trust strategy over trust model, and the advantage of using fuzzy representation of trust and reputation.     

It’s complicated: explaining the relationship between trust,distrust, and ambivalence in online transaction relationships using polynomial regression analysis and response surface analysis

Trust and distrust are considered crucial elements affecting online relationships – particularly those involving electronic transactions. Although some studies propose that they are distinct, others claim that they are merely opposite ends of one continuum. Further adding to the debate is the possibility of ambivalence, a topic that has not been examined in electronic transaction relationships. Unfortunately, current models of trust and distrust have limitations that impede explanations of how – or even if – ambivalence is generated by feelings of trust and distrust and how these two constructs can best coexist. We thus propose a hybrid model which considers the limitations and strengths of previous models. Namely, we posit that trust and distrust can coexist as separate components with related continua. We use polynomial regression analysis (PRA) and response surface analysis (RSA) to test these complex relationships. Using an empirical study of online consumer behaviour with 521 experienced online consumers, strong empirical validation is found for the model. We examine the effects of ambivalence on the truster’s intentions towards a website and find a small positive effect which increases such intentions. PRA and RSA confirm that trust and distrust are most likely separate components – not opposite ends of a continuum – with related continua. The continua within the subconstructs of trust and distrust likely have more complex and interesting relationships than have been considered previously. These findings lead to interesting future research opportunities on trust, distrust and ambivalence using advanced techniques such as PRA and RSA.     

An integrated trust and reputation model for open multi-agent systems

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent’s likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent’s environment.     

Developing and maintaining clients’ trust through institutional mechanisms in online service markets for digital entrepreneurs: A process model

Crowdsourcing intermediaries, such as Upwork and ZBJ, are powering a new force of digital entrepreneurs by creating online service markets where service providers seek contracts from clients all over the world. An important reason for this flourishing digital entrepreneurship is that the intermediaries establish institutional mechanisms (IMs) that develop clients’ trust in digital entrepreneurs. However, insights into how intermediaries achieve it remain limited. Although the e-Commerce literature has generated rich findings on intermediary-based trust development, its findings are based on online product markets and thus may not be readily applicable to online service markets. More specifically, these findings are mainly focused on various IMs that facilitate initial trust formation based on variance models. However, a successful service transaction requires not only the initial trust but further development and maintenance of that trust, because it is a cooperative process between clients and vendors. By conducting an in-depth case study of ZBJ, the largest crowdsourcing intermediary in China, this study develops a three-phase process model, whereby the intermediary deploys different sets of IMs that initiate, augment, and maintain clients’ trust in vendors. Our findings make important theoretical contributions to the literature on digital entrepreneurship, crowdsourcing, and e-Commerce trust development.     

A comparative study of collaboration-based reputation models for social recommender systems

Today, people increasingly leverage their online social networks to discover meaningful and relevant information, products and services. Thus, the ability to identify reputable online contacts with whom to interact has become ever more important. In this work we describe a generic approach to modeling user and item reputation in social recommender systems. In particular, we show how the various interactions between producers and consumers of content can be used to create so-called collaboration graphs, from which the reputation of users and items can be derived. We analyze the performance of our reputation models in the context of the HeyStaks social search platform, which is designed to complement mainstream search engines by recommending relevant pages to users based on the past experiences of search communities. By incorporating reputation into the existing HeyStaks recommendation framework, we demonstrate that the relevance of HeyStaks recommendations can be significantly improved based on data recorded during a live-user trial of the system.     

Why the first provider takes it all: the consequences of a low trust culture on pricing and ratings in online sourcing markets

Electronic markets are ruled by price and reputation, and, at least in the case of Online Sourcing Markets (OSM), also by preference for providers the buyer already contracted with. OSM are online markets for software development. Adding Fukuyama's notion of a low trust culture, an argument is advanced why buyers in OSM may give absolute preference to providers with whom they had previous contracts, presenting a special case of neoclassical contracting. Examining all the transactions in one calendar year at a leading OSM supports this proposition. All it took to be given the tender was to be the only bidding provider with at least one successful previous contract with the buyer, rendering pricing and ratings immaterial to bid choice. Only when none of the bidding providers had previous successful projects with the buyer did pricing and rating affect bid choice. The proposition is also consistent with the buyers’ comments about their providers. Implications are discussed about how a low trust culture affects OSM behavior.     

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。     

Trust transference in brick and click retailers: An investigation of the before-online-visit phase

We examined the formation of online trust encountered by potential customers of a brick and click retailer before they visit its online website; this phase of the purchasing experience has been under-investigated in trust literature. Since a brick and click retailer is eager to attract offline customers to its website based on trust in the brick and mortar stores, our exploratory study investigated, (1) Can customers’ offline trust in a retailer affect their online trust (in the website operations of the retailer)?; (2) What other factors might be influential on online trust? A social relations and networks perspective was adopted to explain customers’ online trust formation during the before-online-visit phase. Findings from a sample of 246 offline customers who had never visited a supermarket's website revealed that word-of-mouth, offline trust, and expected sanctioning power were significant in forming online trust. Furthermore, findings showed that word-of-mouth was more influential than offline trust based on a customers’ personal experience with the supermarket's physical stores. This provided empirical evidence on how and why some pure online retailers outperformed brick and click retailers.     

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.     

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.     

Integrating trust measures in multiagent systems

Several models have been proposed in the past for representing both reliability and reputation. However, we remark that a crucial point in the practical use of these two measures is represented by the possibility of suitably combining them to support the agent's decision. In the past, we proposed a reliability–reputation model, called RRAF, that allows the user to choose how much importance to give to the reliability with respect to the reputation. However, RRAF shows some limitations, namely: (i) The weight to assign to the reliability versus reputation is arbitrarily set by the user, without considering the system evolution; (ii) the trust measure that an agent a perceives about an agent b is completely independent of the trust measure perceived by each other agent c, while in the reality the trust measures are mutually dependent. In this paper, we propose an extension of RRAF, aiming at facing the limitations above. In particular, we introduce a new trust reputation model, called TRR, that considers, from a mathematical viewpoint, the interdependence among all the trust measures computed in the systems. Moreover, this model dynamically computes a parameter measuring the importance of the reliability with respect to the reputation. Some experiments performed on the well‐known ART(Agent Reputation and Trust) platform show the significant advantages in terms of effectiveness introduced by TRR with respect to RRAF. © 2011 Wiley Periodicals, Inc.     

Parameter learning of personalized trust models in broker-based distributed trust management

Distributed trust management addresses the challenges of eliciting, evaluating and propagating trust for service providers on the distributed network. By delegating trust management to brokers, individual users can share their feedbacks for services without the overhead of maintaining their own ratings. This research proposes a two-tier trust hierarchy, in which a user relies on her broker to provide reputation rating about any service provider, while brokers leverage their connected partners in aggregating the reputation of unfamiliar service providers. Each broker collects feedbacks from its users on past transactions. To accommodate individual differences, personalized trust is modeled with a Bayesian network. Training strategies such as the expectation maximization (EM) algorithm can be deployed to estimate both server reputation and user bias. This paper presents the design and implementation of a distributed trust simulator, which supports experiments under different configurations. In addition, we have conducted experiments to show the following. 1) Personal rating error converges to below 5% consistently within 10,000 transactions regardless of the training strategy or bias distribution. 2) The choice of trust model has a significant impact on the performance of reputation prediction. 3) The two-tier trust framework scales well to distributed environments. In summary, parameter learning of trust models in the broker-based framework enables both aggregation of feedbacks and personalized reputation prediction.