Web 服务选择中信誉度评估方法

在开放的Web服务环境中,由于无法保证用户使用服务后给出的反馈等级是真实可靠的,导致服务的信誉度评估结果与实际值存在较大偏差,进而服务选择失败率较高.为了克服上述问题,提出了一种用于Web服务选择的信誉度评估方法.该方法的主要思想是,通过反馈核查、校正和检测这3个信誉度评估模块对服务的信誉度进行评估.仿真结果表明,所提出的方法不仅有效提高了信誉度评估的客观性,还显著减小了服务选择的偏离度.     

COMBINING TRUST MODELING AND MECHANISM DESIGN FOR PROMOTING HONESTY IN E‐MARKETPLACES

In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e‐marketplaces that is attractive to users, through its promotion of honesty.     

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.     

Fairness-guaranteed reputation propagation in Web service composition

In service-oriented environment, it is difficult to evaluate component services because of the opaque characteristic of composite services, the complex invocation structures and the subjective reputation rating of service consumers. To address these issues, this paper proposes a reputation propagation algorithm for service composition, in which the subjective ratings can be fairly propagated to each component service. The algorithm first models service composition as the Beta-mixture, and learns the reputation and responsibility of each component by the EM algorithm. Then, based on the characteristics of Shapley values in cooperative gaming theory, the algorithm computes the contribution of each component to its composition, ensuring that no component would obtain extra rewards or punishments. Finally, theoretical analysis and experimental results demonstrate the fairness of the algorithm to hieratically propagate the consumer's rating to each component service.     

加强房地产企业管理的思考

介绍了房地产企业声誉管理的内涵及其重要性 ,结合房地产企业声誉管理的现状 ,从提升房地产企业经营理念 ,强化声誉决策管理等方面 ,提出了加强房地产企业声誉管理的对策和建议     

A Multi-dimensional Trust-aware Cloud Service Selection Mechanism Based on Evidential Reasoning Approach

In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.     

物联网中跨域移动节点的信任迁移

针对物联网中移动节点跨域后信任缺失的问题,参考当前信任领域和传感器网络的研究成果,提出物联网中面向跨域移动节点信誉体系的网络模型、服务模型和信誉模型,并在此基础上研究异动节点跨域后信任的迁移和建立过程.结合高斯分布的传感器网络信誉模型(GRFSN),讨论跨域移动节点的信誉初始化、信誉更新、信誉整合和信誉共享方法.仿真结果证明,在物联网节点跨域环境下,跨域移动节点的信誉收敛速度快于传统GRFSN模型,并能保持GRFSN模型可检测恶意节点的特点.     

基于仿真的P2P网络信誉模型

信誉机制是解决P2P网络可用性问题的重要途径。在信誉机制的研究中,信誉模型的研究是一项核心工作,已取得了众多研究成果。然而,当前信誉模型在构造方式上存在的广泛差异性,决定了难以对其进行有效的仿真和评价,这也成为制约信誉机制进一步改进和完善的一个重要问题。本文立足于对当前P2P信誉模型的研究与分析,采用理论分析与仿真实验相结合的方法,发掘信誉机制设计要素所产生的影响。     

在P2P网络中基于簇的全局信誉模型的仿真

P2P网络中存在着大量自私行为和欺诈行为,而现有的信任模型无法真实有效地衡量节点的可信度.针对上述问题,提供了一种快速、安全、优质的信任模型.将簇引入信誉模型制定了一系列管理规则,并将多维度引入信誉算法.各节点遵循管理规则自发形成三种不同的簇域,簇域等级越高,簇内节点服务和被服务的机会和优先权就越高.使用信任度、网络贡献度、累计滥用信任3个参数来计算节点的全局信誉.通过进行仿真和实验分析,验证了信誉模型的合理性和对恶意行为的防御能力.     

校园网程控交换机连网故障断点检测技术研究

当前校园网程控交换机连网故障断点检测技术检测准确率较低、误警率高,也无法实现对故障断点区域的精确定位,提出一种基于节点信誉感知模型的连网故障断点检测技术研究。依据校园网的网络层次结构布局,构建节点之间的信誉感知模型,以提取节点故障区域特征信息,基于小波算法对感知信誉数据进行预处理。为提高算法的稳定性,对邻近节点之间的健康程度进行分析,以最终实现对校园网程控交换机连网故障断点的准确定位与检测。仿真实验数据表明,提出的断点故障检测技术在节点感知故障率为0.6的条件下,仍可获得86.32%的检测率。