Towards a trust,reputation and recommendation meta model

New trust, reputation and recommendation (TRR) models are continuously proposed. However, the existing models lack shared bases and goals. For this reason, in this work we define an innovative meta model to facilitate the definition and standardization of a generic TRR model. Following the meta model, researchers in the field will be able to define standard models, compare them with other models and reuse parts of them. A standardization is also needed to determine which properties should be present in a TRR model.In accordance with the objectives we were seeking, following our meta model, we have defined a pre-standardized TRR model for e-commerce, identified the fundamental concepts and the main features that contribute to form trust and reputation in that domain, respected the dependence on the context/role of trust and reputation, aggregated only homogeneous trust information; listed and shown how to defend from the main malicious attacks.     

一种基于风险和推荐的用户信任计算方法

结合客观的风险评估和主观的推荐信任共同计算用户可信度,并利用推荐可信度和域可信度来识别和惩罚提供虚假反馈的服务方,提出了基于风险和推荐的用户信任计算方法。仿真实验表明,该模型具有较好的动态适应性,能够准确反映用户行为,为信任决策提供安全、可靠的依据。     

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.

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.     

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.     

Reputation Formalization for an Information–Sharing Multi–Agent System

We propose that through the formalization of concepts related to trust, a more accurate model of trust can be implemented. This paper presents a new model of trust that is based on the formalization of reputation. A multidisciplinary approach is taken to understanding the nature of trust and its relation to reputation. Through this approach, a practical definition of reputation is adopted from sociological contexts and a model of reputation is designed and presented.
Reputation is defined as role fulfillment. To formalize reputation, it is necessary to formalize the expectations placed upon an agent within a particular multi–agent system (MAS). In this case, the agents are part of an information–sharing society. Five roles are defined along with the ways in which these roles are objectively fulfilled. Through the measurement of role fulfillment, a vector representing reputation can be developed. This vector embodies the magnitude of the reputation and describes the patterns of behavior associated with the direction of the vector.
Experiments are conducted to verify the sensibility of the proposed models for role fulfillment and overall reputation. The simulation results show that the roles, defined for building reputation in an information–sharing MAS environment, react to different agent and user actions in a manner consistent with the formal definitions.     

基于用户及其行为社会属性的信任测度模型

信任测度是信任机制的核心和基础,现有的信任机制面临着恶意用户操纵信誉的安全威胁。基于用户及其行为社会属性的信任测度模型对传统的信任机制进行了扩充,引入用户及其行为所映射的本质特性即社会属性来描述和分析恶意用户及其行为的特征,在信任测度过程中增加信誉评审过程来修正对信任测度的攻击,从而保证了分布式环境中的信任测度的可信性。模拟实验表明,该信任测度模型能有效地应对恶意用户对信誉的操纵攻击。     

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.     

Reputation-based decisions for logic-based cognitive agents

Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification of axioms as first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models.     

多Agent对抗环境下联盟形成的信任模型

提出了一种用于多Agent对抗环境下联盟形成的信任模型CORE,模型从能力和名誉两个方面来描述Agent的信任度,用能力向量空间中的距离公式来度量Agent胜任具体任务的能力大小,用隶属度函数来描述Agent的名誉。任务开始时,模型按信任度大小选取合适的Agent形成联盟,任务中Agent能力动态增长,名誉动态变化,任务结束后,模型根据联盟中Agent的表现情况计算新的信任度,作为下一个任务来临时联盟形成的依据。最后,在NetLogo平台上模拟实现了该模型。     

Agent动态交互信任预测与行为异常检测模型

在agent理论中,信任计算是一个有意义的研究方向.然而目前agent信任研究都是以平均交互成功率来计算,较少考虑信任动态变化,因而准确预测和行为异常检测的能力不能令人满意.针对上述问题,以概率论为工具,按时间分段交互历史给出agent交互信任计算模型CMAIT;结合信任的变化率,给出信任计算的置信度和异常检测机制.实验以网上电子商务为背景,实验结果显示该计算模型的预测误差为TRAVOS的0.5倍,计算量也较少;既可用于对手历史行为的异常检测,防止被欺骗,又可用于对手未来行为的预测.改进了Jennings等人关于agent信任的工作.     

Semantic inference of user’s reputation and expertise to improve collaborative recommendations

Collaborative recommender systems select potentially interesting items for each user based on the preferences of like-minded individuals. Particularly, e-commerce has become a major domain in these research field due to its business interest, since identifying the products the users may like or find useful can boost consumption. During the last years, a great number of works in the literature have focused in the improvement of these tools. Expertise, trust and reputation models are incorporated in collaborative recommender systems to increase their accuracy and reliability. However, current approaches require extra data from the users that is not often available. In this paper, we present two contributions that apply a semantic approach to improve recommendation results transparently to the users. On the one hand, we automatically build implicit trust networks in order to incorporate trust and reputation in the selection of the set of like-minded users that will drive the recommendation. On the other hand, we propose a measure of practical expertise by exploiting the data available in any e-commerce recommender system – the consumption histories of the users.     

EVALUATING THE ReGreT SYSTEM

The ReGreT system is a trust and reputation mechanism that uses, among other things, social information to improve the calculation of trust and reputation measures. Using a framework called SuppWorld designed to test these kind of complex models, we present a set of experiments that evaluate different features of the ReGreT system in several scenarios.     

PerContRep: a practical reputation system for pervasive content services

Social network has extended its popularity from the Internet to mobile domain. Personal mobile devices can be self-organized and communicate with each other for instant social activities at any time and in any places to achieve pervasive social networking (PSN). In such a network, various content information flows. To which extent should mobile users trust it, whilst user privacy can also be preserved? Existing work has not yet seriously considered trust and reputation management, although trust plays an important role in PSN. In this paper, we propose PerContRep, a practical reputation system for pervasive content services that can assist trustworthy content selection and consumption in a pervasive manner. We develop a hybrid trust and reputation management model to evaluate node recommendation trust and content reputation in the context of frequent change of node pseudonyms. Simulations show the advantages of PerContRep in assisting user decisions and its effectiveness with regard to unfair rating attack, collaborative unfair rating attack, on-off attack and conflict behavior attack. A prototype system achieves positive user feedback on its usability and social acceptance.     

Understanding Perceived Trust to Reduce Regret

Trust is fundamental for promoting the use of online services, such as e‐commerce or e‐health. Understanding how users perceive trust online is a precondition to create trustworthy marketplaces. In this article, we present a domain‐independent general trust perception model that helps us to understand how users make online trust decisions and how we can help them in making the right decisions, which minimize future regret. We also present the results of a user study describing the weight that different factors in the model (e.g., security, look&feel, and privacy) have on perceived trust. The study identifies the existence of a positive correlation between the user's knowledge and the importance placed on factors such as security and privacy. This indicates that the impact factors as security and privacy have on perceived trust is higher in users with higher knowledge.     

一种自约束信誉更新模型

在分析信誉计算模型的基础上,提出了一种自约束的信誉更新模型,由于节点的信誉主要反映历史信誉,信誉变化主要表现在新的评价分与以前信誉的变化上,因此通过自约束因子来表达这种变化。对该模型与Bayesian模型作了仿真分析比较。结果显示：信誉评估模型体现恶意行为的惩罚思想,当一个可信节点连续出现交易失败或不可信行为时,信誉值会指数级下降,恢复其信誉则是缓慢上升。     

基于证据理论信任模型的众包质量监控

针对现有众包系统不能快速有效地检测众包交互过程中的恶意行为现状,从信誉角度提出基于证据理论的信任评估模型(DS_CQC)来实现众包平台的质量监控。首先,基于时间窗口获得持续可信证据序列和持续不可信证据序列;其次,从证据重要性、证据间关联和证人可信度三方面对原始D-S证据理论进行改进,获得改进的基本概率信度函数;最后,利用改进的D-S证据理论对证据序列进行融合,计算其直接信誉和间接信誉,最终获得接包方的综合信誉。模型中引入奖惩机制,用以激励接包方参与众包并提供高质量众包,同时遏制恶意的接包方。通过仿真实验和对真实众包数据的实验表明,与基于概率的信任模型相比,DS_CQC检测出持续恶意工作者、策略性恶意工作者的速度和效率至少分别提高了50%和3.1%,具有较强的抗攻击能力。     

Trusted Grid Computing with Security Binding and Trust Integration

Trusted Grid computing demands robust resource allocation with security assurance at all resource sites. Large-scale Grid applications are being hindered by lack of security assurance from remote resource sites. We developed a security-binding scheme through site reputation assessment and trust integration across Grid sites. We do not treat the trust factor deterministically. Instead, we apply fuzzy theory to handle the fuzziness or uncertainties behind all trust attributes. The binding is achieved by periodic exchange of site security information and matchmaking to satisfy user job demands. PKI-based trust model supports Grids in multi-site authentication and single sign-on operations. However, cross certificates are inadequate to assess local security conditions at Grid sites. We propose a new fuzzy-logic trust model for distributed trust aggregation through fuzzification and integration of security attributes. We introduce the trust index of a Grid site, which is determined by site reputation from its track record and self-defense capability attributed to the risk conditions and hardware and software defenses deployed at a Grid site. A Secure Grid Outsourcing (SeGO) system is designed for secure scheduling a large number of autonomous and indivisible jobs to Grid sites. Significant performance gains are observed after trust aggregation, which is evaluated by running scalable NAS and PSA workloads over simulated Grids. Our security-binding scheme scales well with increasing user jobs and Grid sites. The new scheme can guide the security upgrade of Grid sites and predict the Grid performance of large workloads under risky conditions. The research work reported here was supported by a NSF ITR Grant 0325409. The paper is significantly extended from preliminary results presented in IFIP International Conference on Network and Parallel Computing (NPC-2004), IEEE International Parallel and Distributed Processing Symposium (IPDPS-2005), and International Workshop on Grid Security and Resource Management (GSRM-2005). The corresponding author is Kai Hwang at the University of Southern California.     

Using asymmetric keys in a certified trust model for multiagent systems

We present a contribution based on encryption to the model for the certification of trust in multiagent systems. The originality of the proposal remains in the use of asymmetric keys that allow the local storage of testimonies with the service agents that were assessed. The aim is to raise the level of efficiency that client agents have when contracting specialized service agents. To reach this objective we make three hypotheses: (i) client agents are able to measure and inform the quality of a service they receive from a service agent; (ii) distributed certificate control is possible if every service agent stores the certificates it receives from its client agents and, (iii) the content of a certificate can be considered safe as long as the public and private keys used to encrypt the certificate remain safe. This approach reduces some weak points of trust models that rely on the direct interaction between service and client agents (direct trust) or those that rely on testimony obtained from client agents (propagated trust). Simulation showed that encrypted certificates of trust improved the efficiency of client agents when choosing their service provider agents. The reason seems to be that the reputation of a given service provider agent is based on the reputation it has among the totality of client agents that used its services.     

Design,development and evaluation of a human-computer trust scale

ABSTRACT

New technologies, data, and algorithms impact nearly every aspect of daily life. Unfortunately, many of these algorithms operate like black boxes and cannot explain their results even to their programmers, let alone to end-users. As more and more tasks get delegated to such intelligent systems and the nature of user interactions with them becomes increasingly complex, it is important to understand the amount of trust that a user is willing to place on such systems. However, attempts at quantifying trust have either been limited in their scope or not empirically thorough. To address this, we build on prior work which empirically modelled trust in user-technology interactions and describe the development and evolution of a human computer trust scale. We present results of two studies (N=118 & N=183) which were undertaken to assess the reliability and validity of the proposed scale. Our study contributes to the literature by (a) developing a multi-dimensional scale to assess user trust in HCI and (b) being the first study to use the concept of design fiction and future scenarios to study trust.