基于推荐的抗攻击电子商务信任模型

针对目前在线信誉系统面临的自我提升攻击、恶意诋毁攻击和漂白攻击三类常见的攻击,提出了一种基于推荐的集中式信任模型。该模型综合了用户间购买行为的相似度以及评价的时效性确定对某件商品的推荐信任值,同时结合基于自身交易经验的直接信任值,来获取对该商品的综合信任度。通过在真实交易数据中注入三类攻击进行仿真,实验结果证明,相对于现有的信誉管理模型,该模型能更好地抵御攻击,为消费者提供更准确的卖家和商品信誉值。     

基于Agent的分布式系统信任模型仿真

针对当前信任模型仿真缺乏理论支撑,仿真过程描述及建模步骤不规范的问题,将多Agent建模仿真方法应用于信任模型的仿真,建立信任模型Agent仿真过程框架.由信任模型微观机制入手,对个体Agent进行设计,建立Agent实体模型;针对信任模型中Agent之间的交互问题设计基于推荐网的Agent协作算法;在充分考虑系统宏观约束的情况下,建立微观Agent到宏观系统之间的联系.通过实例验证了该方法的有效性.     

基于信用和关系网的Multi-agent System信任体系

提出了一种MAS环境下基于信用和关系网的Agent信任体系，给出了模型的构建方法及相关的量化公式。该信任体系可以使系统中的推荐信任和直接信任等同看待，能抑制恶意推荐、协同作弊这些干扰现象，能促进关系网的凝聚性，简化Agent合作伙伴的选取，从而促进系统中Agent间的合作。     

多Agent系统中基于狄利克雷分布的信任模型

提出一种多Agent系统中基于狄利克雷分布的信任模型.该模型利用狄利克雷分布解决二元评价的局限性,使信任模型可以按等级来评价信誉.提出层次过滤算法,以解决推荐信息中存在的各类恶意Agent问题.仿真实验结果表明,该信任模型能有效抑制不诚实推荐和策略性欺骗.     

多Agent系统中信任的动态性处理

信任是多Agent系统中进行决策和交互的重要内容。收集必要的信息确定信任关系,动态地管理、维护信任关系,以及监控和重估已有的信任关系是多Agent系统中信任管理的关键问题。虽然研究者对上述关键问题提出了一系列解决方案,但依然存在一些问题有待进一步解决。本文针对信任的动态性处理这一问题,在分析现有的典型信任模型基础之上,提出一个具有动态性的Confidence-Reputation信任模型。模型中,我们不仅考虑了Agent的直接交互历史(Confidence)和信誉(Reputation),同时也考虑了信任的本体性。     

直接评价节点诚信度的P2P动态信任模型

为解决现有P2P信任模型计算开销大、动态适应能力差、推荐可信度的动态性未得到足够重视等问题,提出了一种新的动态信任模型。通过直接交互节点的局部评价加权其推荐可信度计算节点的全局信誉值,避免了迭代过程,降低了网络开销。采用基于时间帧的方法更新节点的全局信誉值和推荐可信度,以抑制节点提供服务和推荐两方面的动态性。仿真实验结果表明,新模型较现有模型在网络开销、节点动态性推荐的抑制等方面有较大改进。     

基于聚类推荐的P2P信任模型*

提出一个基于聚类推荐的信任模型。通过聚类算法,对目标节点的评价值进行聚合,使用聚类产生的推荐拟合度以及推荐节点自身全局信誉值共同作为推荐权重,经过迭代合成节点的全局信誉值。仿真实验分析表明,与类似的迭代式信任模型相比,由于推荐权重的分配更合理,从而能更有效地缓解节点恶意行为带来的影响,特别是遏制不诚实推荐节点对信任模型的破坏。     

分布式信任管理体系结构研究与实现

近年来研究者着重于信任管理模型的研究,如何把信任管理模型集成到分布式应用系统中,需要深入探讨。在分析现有的信任管理体系结构基础上,结合面向服务的体系结构(SOA)和信任管理模型的固有特性,提出了分布式应用环境中的信任管理模型体系结构。同时,提出了基于相对熵的推荐信息过滤方法。在多代理仿真平台RePast上,实现了提出的体系结构和推荐信息过滤方法,并与其他推荐信息过滤方法进行了比较。     

多Agent系统中基于认知的信任框架研究

基于认知角度提出了一种Agent间以预动(proactive)的方式建立信任的形式化框架.在框架中首先区分了代理与非代理情形下的信任并分别给出定义.从信任的定义出发,施信方(the trustor)针对信任建立基于认知的推理过程,并根据推理需要主动向受信方请求信息.在获得所需信息后,考虑到交互信息的可靠性问题,施信方在认知推理的基础上进行关于可靠性的模糊推理,并决定是否建立信任.通过这个框架,Agent间可以在缺乏直接交互经验或者第三方证言的情况下,以预动的方式动态地建立信任,并且在信任建立的过程中,可以纳入复杂的上下文约束.同时,通过认知推理与模糊推理的结合,可以根据场景的需要采用不同的规则,给信任的建立带来更大的灵活性.     

用户非对称信任关系的推荐算法

数据稀疏性是目前协同过滤面临的主要挑战之一。用户间的信任关系为推荐系统提供了有用的附加信息。已有工作主要采用直接信任关系作为附加信息,对间接信任关系考虑得较少。针对这一情况,提出一种融合直接和间接的用户非对称信任关系的推荐算法(ATRec)。首先,构建一种信任传递机制,并利用该机制获得用户间的间接非对称信任关系。其次,根据直接和间接非对称信任关系获得每个用户的信任集合。最后,利用信任集合、最近邻的评分和好评阈值计算出商品的受欢迎程度,进而获得对用户的top-N推荐列表。在真实数据集上的实验结果表明,该算法比主流的推荐算法在top-N推荐性能上有更好的表现。     

基于分布式信任管理的时延容忍网络数据转发方案

为解决时延容忍网络中恶意或自私节点严重影响网络性能的问题,提出一种基于分布式信任管理的时延容忍网络数据转发方案。通过计算节点信任值先滤除网络中的恶意节点,再执行数据转发行为。根据节点的转发行为以及它们的能量消耗信息计算出直接信任;邻居节点推荐的信息融合间接信任和推荐信誉计算出推荐信任;融合直接信任和推荐信任得到节点总体信任,并利用信任记录窗口对节点信任值进行更新。仿真结果表明:所提方案能有效消除时延容忍网络路由的不正当行为,在数据包传递率、开销和时延等性能上均优于RBTM方案、CWS方案和SPRAY方案。     

FUZZY TRUST AGGREGATION AND PERSONALIZED TRUST INFERENCE IN VIRTUAL SOCIAL NETWORKS

Virtual marketplaces on the Web provide people with great facilities to buy and sell goods similar to conventional markets. In traditional business, reputation is subjectively built for known persons and companies as the deals are made in the course of time. As it is important to do business with trustful individuals and companies, there is a need to survive the reputation concept in virtual markets. Auction sites generally employ reputation systems based on feedbacks that provide a global view to a cyber dealer. In contrast to global trust, people usually infer their personal trust about someone whose reputation is completely or partially unknown by asking their trusted friends. Personal reputation is what makes a person trusted for some people and untrusted for others. There should be a facility for users in a virtual market to specify how much they trust a friend and also a mechanism that infers the trust of a user to another user who is not directly a friend of her. There are two main issues that should be addressed in trust inference. First, the trust modeling and aggregation problem needs to be challenged. Second, algorithms should be introduced to find and select the best paths among the existing trust paths from a source to a sink. First, as trust to a person can be stated more naturally using linguistic expressions, this work suggests employing linguistic terms for trust specification. To this end, corresponding fuzzy sets are defined for trust linguistic terms and a fuzzy trust aggregation method is also proposed. Comparing the fuzzy aggregation method to the existing aggregation methods shows superiority of fuzzy approach especially at aggregating contradictory information. Second, this paper proposes an incremental trust inference algorithm. The results show improvement in preciseness of inference for the proposed inference algorithm over the existing and recently proposed algorithm named TidalTrust.     

一个信任和声誉模型及其应用

购物Agent在基于Web的电子市场中携带着用户的需求选择合适的销售者,在信息不完全的情况下,购买者需要利用信任和声誉在销售者中选择自己信任并能给自己带来较高效用的销售者进行协商。论文提出了一个信任和声誉度量模型,并为购物Agent提出了一个建立信任和声誉模型的算法,购物Agent以协商历史为基础利用这个算法来选择适合自己的销售者,从而提高协商效率,最后给出一个实例的分析。     

基于多Agent的医疗信任模型的模拟

结合直接交互信任评价和第三方信誉推荐,提出了一种基于多Agent技术的医疗信任模拟模型。使用置信因子来综合直接交互信任值和第三方信誉推荐值;避免了患者直接交互的盲目性和巨额计算,完善了对医生自我推荐的辨别能力。实验采用Swarm仿真平台模拟医疗信任模型的交互过程,结果表明该模型在确定信任关系中相对IT模型要快,比第三方信誉推荐模型相对要可靠。     

基于智能体和数据仓库的群决策支持系统

传统的群决策支持系统存在着群体决策活动协调力度差、数据共享困难等缺陷，不能很好的支持Web下的群体决策活动。文中在分析多智能体和数据仓库概念的基础上，提出了一个基于多智能体和数据仓库的群体决策支持系统，利用数据仓库和智能体技术解决了群体决策支持系统中的信息集成和分布式问题求解问题，并对基于智能体的群决策过程进行了研究。     

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.     

Behavior-based reputation management in P2P file-sharing networks

Trust research has become a key issue in the last few years as a novel and valid solution to ensure the security and application in peer-to-peer (P2P) file-sharing networks. The accurate measure of trust and reputation is a hard problem, most of the existing trust mechanisms adopt the historical behavior feedback to compute trust and reputation. Thus exploring the appropriate transaction behavior becomes a fundamental challenge. In P2P system, each peer plays two roles: server and client with responsibility for providing resource service and trust recommending respectively. Considering the resource service behavior and trust recommending behavior of each peer, in this paper, we propose a new trust model adopting the technology to calculate eigenvectors of trust rating and recommending matrices. In our model, we define recommended reputation value to evaluate the resource service behavior, and recommending reputation value to evaluate the trust recommendation behavior. Our algorithm would make these two reputation values established an interrelated relation of reinforcing mutually. The normal peers provide authentic file uploading services, as well as give correct trust recommendation, so they can form a trusted and cooperative transaction community via the mutual reinforcement of recommended and recommending reputation values. In this way, the transaction behaviors of those malicious peers are isolated and confined effectively. Extensive experimental results also confirm the efficiency of our trust model against the threats of exaggeration, collusion, disguise, sybil and single-behavior.     

P2P网络环境下基于推荐的信任模型

基于网络中节点之间不仅仅局限于直接交易建立起来的信任关系,还包括了第三方推荐信任的事实,提出了在P2P网络环境下基于推荐的信任模型。该模型用成功次数与失败次数在总交易数目中的比例作为直接信任度,将交易信誉与推荐信誉明确区分出来,引入了偏移因子计算推荐节点的可信性,通过惩罚因子和风险因素动态平衡节点直接信任度和其他节点的推荐信任度,得到目标节点的综合信任值,并给出仿真实验验证。实验结果证明,模型计算的综合信任值更趋近其真实值,并且能抵抗恶意节点的诋毁、协同作弊等威胁。     

基于交易金额的认证信誉

认证信誉是目前信任模型的一个重要组成部分,它使Agent利用很小的开销就可以跟合作伙伴建立起信任关系.然而,由于在评价的计算过程中忽略了交易金额这个重要因素,使得认证信誉不能够很好的处理虚假信息所造成的影响.因此,提出了基于交易金额的认证信誉来解决这个问题,实验表明基于交易金额的认证信誉能够帮助Agent选择更合适的交易伙伴.     

开放网络中基于群组的主观信任模型

针对现有信任模型不能很好地解决信任信息有效共享问题.从模拟人类社会信任建立过程出发,提出了开放网络中基于群组的信任模型,并给出了信任初始化算法以及对自私节点和不诚实节点的惩罚机制.最后,通过仿真实验验证模型能够有效地收集推荐信息,同时减少恶意推荐对信任合成的影响.