一种可信网络中敏感的直接信任度计算模型

借鉴人类信任关系的遗忘、衰减、相对稳定又动态敏感、敏感性的非对称性和空时联合等特征,提出一种具有双滑动窗口机制的敏感的直接信任度计算模型。采用一较大的稳定性窗口获取直接信任度的稳定性部分,采用一较小的敏感性窗口获取其敏感性部分。在敏感性窗口内,分别采用非对称地提高负向波动的交易满意度权重方法和自适应地改变窗口大小的方法,刻画直接信任度对交易满意度波动的空时联合特征。仿真实验表明,所提出的直接信任度模型既相对稳定又对节点行为变化单边动态敏感,且这种敏感性具有明显的空时联合特性。     

基于蚁群算法的加强型可抵御攻击信任管理模型

通过将网络节点推荐行为分析和网络恶意节点密度的自适应机制纳入信誉度评价过程,提出了基于蚁群算法的加强型可抵御攻击信任管理模型--EAraTRM,以解决传统信任模型因较少考虑节点的推荐欺骗行为而导致容易在恶意节点的合谋攻击影响下失准的问题。在对比研究中发现,EAraTRM可以在网络中恶意节点密度达到90%,其他传统信任模型已经失效的情况下,仍保持较高的正确性。实验结果表明,EAraTRM能提高节点评价其他节点信誉度时的精度,并降低整个网络中恶意节点间进行合谋攻击的成功率。     

Explanation-aware service selection: rationale and reputation

A collection of 1 billion¹ publicly available web services can form an internet-scale infrastructure for building diverse applications. For a given application, selection of services and service providers from this collection becomes important and reputation is recognized as a key factor for this purpose. However, current reputation systems are limited in their ability to exchange reputation information between heterogeneous systems. To facilitate meaningful exchange and reuse of reputation information and for the overall determination of reputation, we identify the need to infer and explicate rationale for ratings. We present our knowledge-based approach to inferring and explicating rationale for ratings. We show that this approach facilitates detection of deception and collusion, user preference elicitation, explication of rationale behind user ratings and generation of personalized service recommendations.     

选择在线交易服务者的多情境信任和声誉评估系统

在以信任和声誉值选择服务者的系统中,普遍存在的问题是在评估服务者的信任和声誉值时考虑因素过少,局限于系统内,以及方法缺少灵活性,导致选择的服务者不能满足用户多样性的要求。为了解决这个问题,提出一种应用于在线交易服务者选择的多情境信任和声誉评估系统,服务者通过注册的质量属性和担保资金获得信任和声誉启动值,由系统内、外的交易经历获得信任和声誉经验值,实际信任和声誉值由两者动态线性组合,后者所占比重随交易次数的增加而动态增加,根据用户设置的服务者的情境属性与实际信任和声誉值选择服务者。通过与其他方法对比测试,结果表明此方法更容易满足用户的各种要求。此信任和声誉评估系统不仅能给新服务者与其他服务者一个公平竞争的环境,也可以减少用户选中恶意服务者的机会。     

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.     

一种基于声誉的评分累积信任模型

安全多方计算为保护各方的私有信息,采用安全协议来保证合作计算的顺利进行。但恶意攻击的存在,使得安全协议的复杂性较高,协议的可操作性较低。鉴于此,提出一种基于声誉的评分累积信任模型,根据参与节点的历史行为评估其声誉,辨别恶意节点,采用惩罚机制鼓励可信的参与节点、隔离不可信节点,从而降低恶意攻击带来的风险。实验表明,该模型可以在一定程度上抵制自私的恶意攻击。     

基于信誉的peer-to-peer推荐信任模型

随着对等网络p2p技术的不断发展,如何在p2p各个对等点之间建立起信任关系,已成为当今p2p技术研究的一个重要课题。在研究一些现有信任模型的基础上,分析其存在的问题,提出一种基于信誉的对等网信任模型,给出了信任度计算的算法．并设计了一种信任查询协议,最后,通过实验验证和分析了模型的可行性和安全性．     

User‐oriented quality for OER: understanding teachers' views on re‐use,quality, and trust

We analysed how teachers as users of open educational resources (OER) repositories act in the re‐use process and how they perceive quality. Based on a quantitative empirical study, we also surveyed which quality requirements users have and how they would contribute to the quality process. Trust in resources, organizations, and technologies seem to be of particular importance when looking at quality. In our findings, we derive recommendations for learning object repositories and OER user‐oriented quality assurance.     

基于名声评价的信任模型研究

根据交易者在交易环境中的角色划分设立商家名声与消费者名声,提出了基于评价时间顺序、交易金额和消费者信任值权重的信任模型;通过金额和消费者信任值加权计算商家加权平均分,在判断消费者评价可信度基础上,考虑时间衰减因子计算商家信任值。实验证明,该信任模型能够准确地判断诋毁消费者和不诚信商家,提高了信任模型的抗攻击能力和可用性,可有效帮助消费者识别商家的信任值变化趋势。     

在线信誉系统中的信任模型构建研究

针对现有在线信誉系统中信任模型的缺陷,基于在线拍卖的特点,并结合已有信任研究成果,构建了基于多影响因素的信任模型．本模型适用于在线拍卖环境下对用户信誉状况的全面、准确的评估．仿真实验表明,与现有信任模型相比,本模型的信任度计算精确度较高,具有较好的可行性．     

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.     

一种服务多维信任属性模型的研究

随着Web服务研究的不断深入,从服务角色的角度出发考虑服务结构已经成为研究的热点问题。主要从服务的使用者、提供者和服务本身三个方面考虑,提出一个服务信任管理模型,通过使用者对服务信任属性的反馈信息修正提供者发布的服务主观信任信息,通过服务组装过程中获得的信任信息修正提供者提供的服务客观信任信息,从而为用户提供更加准确的基于信任的服务推荐。     

A reputation assessment model for trustful service recommendation

Nowadays, software systems are mainly Web front-based, Cloud-deployed and accessible by a wide audience over the Internet. These online systems commonly rely on Service-oriented Architecture principles, where they are built as orchestrations of RESTful (and in some rare cases as SOAP-based) services. Integrating new services in an existing orchestration is a challenging and risky task because trustworthiness of these services is not guaranteed throughout their lifetime. Reputation of services is a good indicator about the overall quality of services, because it reflects consumer satisfaction regarding the service-offered functionality and quality. Thus, reputation of services could be considered in the selection and recommendation of trustworthy services. In this paper, we present a framework for the management of web service reputation to conduct a better service recommendation. We present a reputation assessment model that aggregates fair user feedback ratings. The model includes a mechanism that prevents the introduction of malicious feedback ratings, by penalizing detected specious users. In addition, this framework includes a bootstrapping technique for estimating reputation of newcomer services based on neighbor similarity and initial advertised QoS. A set of experiments has been conducted to evaluate the effectiveness of the proposed framework. The results of these experiments highlighted the potential of our framework. These are presented at the end of the paper.     

WSB: a broker‐centric framework for quality‐driven web service discovery

Web service interfaces can be discovered through several means, including service registries, search engines, service portals, and peer‐to‐peer networks. But discovering Web services in such heterogeneous environments is becoming a challenging task and raises several concerns, such as performance, reliability, and robustness. In this paper, we introduce the Web Service Broker (WSB) framework that provides a universal access point for discovering Web services. WSB uses a crawler to collect the plurality of Web services disseminated throughout the Web, continuously monitor the behavior of Web services in delivering the expected functionality, and enable clients to articulate service queries tailored to their needs. The framework features ranking algorithms we have developed which are capable of ranking services according to Quality of Web Service parameters. WSB can be seamlessly integrated into the existing service‐oriented architectures. Copyright © 2010 John Wiley & Sons, Ltd.     

一种ad hoc网信任声誉评估机制

在Ad hoc网络中,设计一种有效的机制来确保信息包的有效传递是研究重点.本文提出了一种信任声誉评估机制,不同于以往根据位置评估声誉和处理恶意节点散播虚假声誉的机制,它采用全局消息来判断,结合两者的优点,实验证明,新的信任声誉评估机制能很好地辨别自私节点和恶意节点,确保信息包有效传递.     

可信网络中一种敏感鲁棒的信任计算模型*

提出一种敏感、鲁棒的信任度计算模型。在缺乏直接交易证据时,基于推荐信任、信誉等间接信任度估计信任度初值,随交易的建立和累积与直接交易证据共同作出信任评价,并最终过渡至直接信任度计算。采用动态平衡系数,兼顾信任度初值估值和直接交易证据小样本风险,逐步降低信任度初值估值风险并将其限制在最初的有限次交易以内;同时增加交易证据对信任度的贡献。在直接信任度计算中,采用双滑动窗口机制,即稳定性窗口N和敏感性窗口M,使信任度保持相对稳定又对交易满意度动态敏感。采用动态权值法赋予明显偏离信任度均值的节点信任度较小权值,提高模型的抗攻击能力。给出了直接信任度稳定性窗口N、敏感性窗口M、稳定性—敏感性平衡系数的建议值。仿真结果验证了本方法的有效性。     

TRIMS,a privacy-aware trust and reputation model for identity management systems

Electronic transactions are becoming more important everyday. Several tasks like buying goods, booking flights or hotel rooms, or paying for streaming a movie, for instance, can be carried out through the Internet. Nevertheless, they are still some drawbacks due to security threats while performing such operations. Trust and reputation management rises as a novel way of solving some of those problems. In this paper we present our work TRIMS (a privacy-aware trust and reputation model for identity management systems), which applies a trust and reputation model to guarantee an acceptable level of security when deciding if a different domain might be considered reliable when receiving certain sensitive user’s attributes. Specifically, we will address the problems which surfaces when a domain needs to decide whether to exchange some information with another possibly unknown domain to effectively provide a service to one of its users. This decision will be determined by the trust deposited in the targeting domain. As far as we know, our proposal is one of the first approaches dealing with trust and reputation management in a multi-domain scenario. Finally, the performed experiments have demonstrated the robustness and accuracy of our model in a wide variety of scenarios.     

可信网络中基于信誉和风险评估的动态信任模型*

针对可信网络不能有效处理恶意节点攻击的问题,综合考虑信任度评估的动态性和风险性,提出一种新的基于信誉和风险评估的动态信任模型。该模型引入惩罚机制、风险机制和推荐实体信任度更新机制,以评估信任关系的复杂性,通过构建直接信任树以存储节点的交互关系,并采用改进的回溯法寻找最佳推荐路径。仿真结果表明,该模型在有效抑制恶意节点上较已有模型有一定的改进。     

Towards a trust evaluation middleware for cloud service selection

With the advent of cloud computing, employing various cloud services to build highly reliable cloud applications has become increasingly popular. The trustworthiness of cloud services is a critical issue that hinders the development of cloud applications, and thus is an urgently-required research problem. Previous studies evaluate trustworthiness of services via either QoS monitoring mechanisms or user feedback ratings, while seldom they combine both of them for enhancing service trust evaluation. This paper proposes a trustworthy selection framework for cloud service selection, named TRUSS. Aiming at developing an effective trust evaluation middleware for TRUSS, we propose an integrated trust evaluation method via combining objective trust assessment and subjective trust assessment. The objective trust assessment is based on QoS monitoring, while the subjective trust assessment is based on user feedback ratings. Experiments conducted using a synthesized dataset show that our proposed method significantly outperforms the other trust and reputation methods.     

上下文感知在Web服务动态信任计算中的应用

针对当前信任模型未能充分考虑环境因素在信任评估中的影响问题,提出了一种基于上下文本体的Web服务动态信任计算模型（CO-WTCM）。该模型通过感知服务实体所处上下文信息,对这些动态、分布、异构的信息进行本体建模,在此基础上采用主客观组合赋权法和模糊综合评估算法对服务实体间的信任关系进行综合评估并通过信任度的综合计算实时更新实体间的信任关系,从而最大程度地保证实体行为的安全和可信。通过与EigenTrust模型相对比,仿真实验验证了该模型的可靠性和有效性。