P2P网络下基于推荐的信任模型

基于推荐信任机制,引入正态概率密度函数的概念,对信任度进行描述。提出一种对信任度进行概率分析的评估方法,可动态地适应用户的安全需求,减弱在多路径推荐中由于恶意实体推荐所带来的负面影响,提高信任计算结果的稳定性。分析和模拟试验表明该模型的必要性和有效性,可以更好地解决P2P网络带来的安全问题。     

P2P网络环境下信任模型的研究

由于对等(P2P)网络的开放性和匿名性,各种恶意节点的恶意行为层出不穷,严重影响了网络的正常运行,加之传统的信任管理模型并不能很好地适应对等网络环境,提出了一种基于分级推荐的P2P网络信任模型(GRTM)。仿真实验表明,基于分级推荐的信任模型能有效评估节点的信任度,交易成功率优于传统的信任管理模型。     

基于信任度的P2P信任机制

提出了一种结合上下文对内容进行信任度的评估方法。信任度由内容信任度和节点信任度两方面计算得到。并构建了存放历史信任度的矩阵,进而提出了资源节点及用户节点双向搜索策略,本机制在P2P网络环境下,能很好的解决冒名及作弊等恶意行为,使主体与客体间的信息交互在信任度的控制下能有效进行。     

进化算法迭代优化的P2P网络信任模型研究

为了解决P2P网络系统中节点频繁退出和加入引起的系统数据信息误差,并针对数据获取对中心节点依赖度较大和系统中节点的数据信息获取不完整等问题,提出了进化算法迭代优化的P2P网络信任模型.首先将P2P网络系统中节点数据信息获取的系统信任度估计模型转化为从源节点到目标节点最优信任关系的路径寻优问题;然后利用改进的粒子群算法对信任关系路径方案进行粒子映射,并通过对粒子粒距聚集度和粒子信息熵进行计算而修正粒子权值,再对粒子局部最优解和全局最优解进行更新;最后迭代的对信任关系路径解空间中的最优解进行搜索,并对最优路径的节点进行推荐信任度加权.仿真结果表明,改进算法具有较好的收敛速度和较强的有效性,且当节点跳级数较少时,可使系统数据信息估算获得最优的系统信任度.     

一种P2P系统中自适应的动态信任度量

P2P系统中信任评估一个重要的挑战是如何有效处理恶意节点的策略性行为改变.现有的信任度量对信任关系的动态性的支持不足,影响了信任评估的准确性和动态适应能力.为此,提出一个自适应的动态信任度量,该模型充分考虑经验和时间因素对信任动态性的影响,引入了自适应遗忘因子和累积滥用信任两个参数来更新节点信任度,并通过反馈控制机制动态调节上述参数,提高了信任模型的动态适应能力.仿真实验表明,和已有的信任模型相比,提出的动态信任度量具有更好的动态适应能力,能够有效处理动态恶意节点策略性的行为改变对系统的攻击.     

一种基于节点推荐可信度的P2P信任模型

提出了一种基于节点推荐可信度的信任模型,通过对推荐节点的服务质量和推荐质量进行区分,实现节点推荐行为的量化评估,最大程度地降低虚假反馈对提供服务节点的诋毁或吹捧,有效保证服务节点全局信任值的真实可靠性。仿真分析表明,该模型可以有效抑制信任模型中不诚实反馈行为的危害性。     

基于交易特征和反馈评价的P2P信任模型

提出了一种新的在P2P系统中基于交易特征和反馈评价的信任评估机制。为确定反馈评价的真实性,模型引入了时间衰减函数和事务影响参数,给出了反馈评价的可信度计算方法。实验仿真结果表明,该评估机制能够提高局部声誉和全局声誉的计算准确性,有效地抑制恶意节点。与信任模型PeerTrust和EigenTrust相比,该模型的性能优于其他两者,能够更好地应用于P2P电子商务系统中。     

P2P网络下信任模型研究

对等网络(Peer-to-Peer,P2P)中信任问题可以通过在系统中建立可靠的信任管理模型来解决。本文首先概述P2P网络及其面临的安全问题,然后对现有的典型P2P网络信任模型进行分析,并对其进行分类研究,最后,讨论该方向上尚未解决的问题和发展趋势。     

P2P环境下的一种混合式信任模型

提出了一种P2P环境下的混合式动态信任模型来解决当前P2P网络的安全性差、难于管理等问题缺陷。该模型融合了本地信任、推荐信任和全局信任模型,通过有机的结合能充分发挥各自模型的优点。同时通过相应的反馈机制能够有效地判断节点信任度的变化和抵御诋毁、夸大等安全问题。仿真结果表明,该模型能有效地判断节点的信任度,同时具有良好的安全性。     

一种基于概率统计方法的P2P系统信任评价模型

现有的P2P系统信任评价模型正面临着两种恶意节点的攻击行为--策略性欺骗和不诚实推荐,严重影响了模型计算节点信任评价的准确性和有效性.针对现有模型存在的不足,提出了一种基于概率统计方法的信任评价模型.该模型借鉴人类社会中主观信任关系的概念,依据直接经验和反馈信息,利用概率统计方法分别计算节点的直接信任和推荐信任,并通过区分直接经验的重要程度,区分反馈信息及其推荐者的可信度,提高信任评价模型的有效性.仿真实验分析说明,与已有的信任评价模型相比,该模型能够更有效地抑制策略性欺骗和不诚实推荐的威胁,特别是复杂的协同作弊方式对系统的攻击.     

Reputation based friend-to-friend networks

Friend-to-Friend networks are a form of Peer-to-Peer networks that provide anonymity of the users who share data. Turtle is an example of such a network, where a peer only connects to real life friends. In this paper we propose an extension of Turtle for connecting to other nodes based on their reputation. Furthermore, we use trust management to dynamically adjust this reputation based on the honest or malicious behaviour of all connected peers. Using these techniques, a node is able to create links to other nodes and identify possible misbehaving friends. Finally, we present simulation results of a reputation based Turtle Friend-to-Friend network.

Enhancing peer-to-peer collaboration using trust

Distributed systems generally require their component parts to interact cooperatively, in order for the system as a whole to function effectively. For any given activity, there are typically several alternative components that have the required capabilities. In decentralised systems, where there is no overarching control, individual components are responsible for selecting other components with which to cooperate. However, the candidate components may be unreliable or dishonest, and are typically locally controlled. Such decentralised systems can be viewed as multi-agent systems, comprising autonomous agents that must cooperate for the system to be effective. Peer-to-peer (P2P) systems are a subclass of decentralised distributed systems, in which not only is there no overarching control, but neither is there any hierarchy of control, power, or responsibly among the system components. Selecting appropriate peers to cooperate with is a challenging problem, since the candidate peers are autonomous and may be unreliable or dishonest. Peers need a mechanism for task delegation that takes the uncertainly of interactions into account. In this paper we present a mechanism, called Mdt-r, that enables peers to delegate activities appropriately, using trust and the recommendations of other peers to meet individual preferences, such as minimising risk and maximising quality.     

Reliable peers and useful resources: Searching for the best personalised learning path in a trust- and recommendation-aware environment

The evolution of the Web 2.0 and the intensive use of peer-to-peer networks allow us to access more and more information from disparate data sources than in the past, thus making life-long learning more effective. In this scenario, a critical issue still remains to be addressed: the reliability of resources, whether they can be recommended as useful and the reliability of peers, whether it is possible to trust them as providers. We propose to integrate these concepts with e-learning, proposing a model for searching for personalised and useful learning paths suggested by reliable (trusted) peers. We performed simulations on the Merlot data set enhanced with information extracted from Advogato, Epinions and Ariadne data sets, testing the efficiency and effectiveness of the proposed approach.     

A contribution-based service differentiation scheme for peer-to-peer systems

Trust is required in a file sharing peer-to-peer system to achieve better cooperation among peers. In reputation-based peer-to-peer systems, reputation is used to build trust among peers. In these systems, highly reputable peers will usually be selected to upload requested files, decreasing significantly malicious uploads in the system. However, these peers need to be motivated by increasing the benefits that they receive from the system. In addition, it is necessary to motivate free riders to contribute to the system by sharing files. Malicious peers should be also motivated to contribute positively by uploading authentic files instead of malicious ones. Service differentiation is required to motivate peers to get involved by sharing and uploading the requested files. To provide the right incentives for peers to contribute to the system, the new concept of Contribution Behavior is introduced for partially decentralized peer-to-peer systems. In this paper, the Contribution Behavior of the peer is used as a guideline for service differentiation instead of peer’s reputation. Both Availability and Involvement of the peer are used to assess its Contribution Behavior. Performance evaluations confirm the ability of the proposed scheme to effectively identify both free riders and malicious peers and reduce the level of service provided to them. On the other hand, good peers receive better service. Simulation results also confirm that based on a Rational Behavior, peers are motivated to increase their contribution to receive services. Moreover, using our scheme, peers must continuously participate, reducing significantly the milking phenomenon.

网格资源选择中推荐信任关系问题的研究

网格资源选择作为网格研究的核心内容,受到高度关注。目前已经提出的基于信誉的模型中,推荐信任问题是信任管理的重要组成部分。本文首先介绍了基于信誉的网格资源选择模型,然后通过深入分析,指出了模型中推荐信任会产生的问题,给出了解决方法,为在网资源选择模型中对推荐信任值的选取提供了一种途径。     

网格域间信任评估模型的研究

在网格域划分的基础上,提出一种域间信任关系评估模型,具有同时兼顾服务的上下文和信任的时衰性的特点,并针对网格环境中的恶意节点和恶意推荐行为,给出相应的应对机制。实验表明,该模型能够有效地解决网格环境中的恶意行为,对恶意节点进行惩罚。     

网格域间信任评估模型的研究

在网格域划分的基础上,提出一种域间信任关系评估模型,具有同时兼顾服务的上下文和信任的时衰性的特点．并针对网格环境中的恶意节点和恶意推荐行为,给出相应的应对机制。实验表明,该模型能够有效地解决网格环境中的恶意行为,对恶意节点进行惩罚。     

对等网络信任度分布式存储的研究

利用结构化对等网络的优点,由网络中的部分节点组成结构化的叠加网络来储存节点信任度的多个拷贝,并提出了节点信任度的加密报告机制和简单的报告消息验证方法,抵抗恶意节点篡改自己或同伙的推荐的企图.这种方法能够在进行全局信任度计算时,将离开系统的节点的评价纳入考虑的范围,并保证在需要使用信任度值时,确定的有限步内找到信任度文件.     

基于内容相似度的对等网络信用模型研究

由于缺乏有效的信用管理机制,对等网络节点间存在着大量的欺诈行为,从而严重影响了整个网络的服务质量.本文在已有模型的基础上提出了一种对等网络信用模型,它把节点共享内容的相似度作为信用评估的重要因素,节点可以根据交易经验自适应地调整与邻居节点的连接,从而提高下载服务的质量.模拟实验结果表明,该模型能够有效处理节点的恶意行为,并且具有良好的扩展性和较低的运行开销.