基于推荐质量的信任感知推荐系统

推荐系统在解决信息过载方面已经取得了很大的成功,同时也存在数据稀疏、冷启动等问题。如何在评分数据稀疏的情况下获得满意的推荐成为推荐系统亟待解决的问题。将信任引入推荐系统成为解决上述问题的有效方法之一。已有的信任感知推荐系统大多基于布尔型信任关系,且没有考虑信任的领域相关性。在服务选择领域,服务请求者依据QoS(quality of service)选择服务。联想到在服务推荐领域推荐请求者可以依据推荐质量(quality of recommendation,QoR)选择推荐用户,提出了推荐质量(QoR)的概念和基于推荐质量的信任感知推荐系统。QoR的属性包含评价相似度、领域信任值、领域相关度和亲密程度,利用信息熵方法可确定各属性的权重。仿真表明该方法提高了推荐系统在数据稀疏情况下的精确度和评分覆盖率,有效提高了冷启动用户的召回率,在一定程度上解决了冷启动问题。     

基于可信计算平台的信任链传递研究进展

信任链传递问题是可信计算的基本问题.阐述了信任链传递在技术与理论方面的最新研究进展.通过分析信任链传递的技术方案、可信测量技术、信任链理论和信任链的可信度度量理论,提出了值得研究的理论与技术方向,包括:以可信静态测量、可信动态测量技术等为代表的信任链传递关键技术,以信任链层次理论模型、信任链传递中的信任损失度量理论和软件的动态可信度度量理论等为代表的基础理论.     

基于性能量化矩阵的计算网格作业调度算法研究

提升计算网格系统运行效率的关键在于作业调度算法,如何综合各种因素使得调度策略更为全面是一个有挑战性的问题.通过建立网格资源性能量化矩阵,构建了一个作业调度模型,并基于此模型给出了一个具体的作业调度算法.通过性能分析和实验仿真,该算法在运行时间、占用资源等方面都有较大的改善,能较好地适应网格系统的动态性和可扩展性特点,提高了作业调度效率.     

Denial of service attacks and defenses in decentralized trust management

Trust management is an approach to scalable and flexible access control in decentralized systems. In trust management, a server often needs to evaluate a chain of credentials submitted by a client; this requires the server to perform multiple expensive digital signature verifications. In this paper, we study low-bandwidth Denial-of-Service (DoS) attacks that exploit the existence of trust management systems to deplete server resources. Although the threat of DoS attacks has been studied for some application-level protocols such as authentication protocols, we show that it is especially destructive for trust management systems. Exploiting the delegation feature in trust management languages, an attacker can forge a long credential chain to force a server to consume a large amount of computing resource. Using game theory as an analytic tool, we demonstrate that unprotected trust management servers will easily fall prey to a witty attacker who moves smartly. We report our empirical study of existing trust management systems, which manifests the gravity of this threat. We also propose a defense technique using credential caching, and show that it is effective in the presence of intelligent attackers. A preliminary version of this paper was presented at the Second IEEE International Conference on Security and Privacy in Communication Networks, Baltimore, MD, USA, August 2006.     

云环境下基于协同推荐的信任评估与服务选择

“模糊、自治”的云计算环境中,服务品类繁多、质量参差不齐,用户主体难以进行可信赖的服务选择。在用户交互经验的基础上,结合现实人际交易模式,提出了一种基于协同推荐的综合信任量化评估模型。模型引入了时间衰减、权重两类动态因子,设计了多元化混合协同推荐算法来实现用户之间的有效协作,帮助用户正确选择可信云服务。为了验证模型的可行性,设计出一个分布式的原型系统,对模型的用户满意度和服务选择质量进行仿真实验。仿真结果表明,该模型能够更快地提高平均服务满意度,更有效地抑制恶意服务,而且随着交互次数的增长,服务选择质量也会不断提高。     

Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

节点信任度模型的算法研究与设计

节点信任反映的是网络中一个节点对另一个节点行为以及能力的综合评判。首先分析了信任模型现在的研 究状况,并在研究本地信任、间接信任、综合信任的基础上,针对节点信任度模型的算法设计,引入概率论的相关知识 来计算节点的可信程度。该方法针对节点信任度的多种影响因素,采用身份认证技术进行了节点信任度的模型设计, 然后对其算法进行了设计实现。     

Online political marketing in Greece: An evaluation of the 2007 national elections and two case studies

This paper presents findings on the use of Web sites as a political marketing tool in the context of the 2007 Greek national elections. The questions guiding this research are how popular were online campaigns in terms of candidate use of Web sites and which trust and credibility-building Web site design cues were leveraged by candidates. This study also explored differences in online campaign practices both between Greek political parties and between the 2007 national Greek elections and the 2002 federal US elections.     

A trust degree based access control in grid environments

The purpose of grid computing is to enable coordinated resource sharing and support cooperative work between different domains in dynamic grid environments. In order to protect each participant’s privilege and security, a secure and efficient access control is essential. This paper presents a new approach of access mechanism based on trust relationships across domains. A new calculation method of trust in grid is proposed and the difference between intro-domain trust and inter-domain trust is analyzed. In addition, a novel access control framework combined with trust degree is given from this proposal. It is shown to be adaptive for both intro-domain and inter-domain conditions. Hence, a prototype system based on the proposed model is introduced; furthermore, it has been shown as a dynamic and fine-granularity access control method through performance analyses and has also been demonstrated as a suitable system for grid environments.     

一种基于Bayes估计的WSN节点信任度计算模型

鉴于传统网络安全策略无法阻止或识别传感器网络内部节点的攻击或异常行为,结合节点资源受限的特点,提出了一种无线传感器网络节点信任度计算模型.该模型采用Bayes估计方法,通过求解基于Beta分布的节点行为信誉函数的期望值得到直接信任并将其作为Bayes估计的先验信息,将来自邻居节点的推荐信息作为其样本信息.仿真实验表明,本方案有较好的稳定性,能够有效识别异常节点,从而阻止内部节点对网络的攻击,与RFSN相比,不仅节约了存储空间、运算时间与通信量,而且能够避免恶评现象对节点信任度计算的影响.