A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Hybrid Trust Based Reputation Mechanism for Discovering Malevolent Node in MANET

A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network(MANET).A MANET’s nodes could engage actively and dynamically with one another.However,MAN-ETs,from the other side,are exposed to severe potential threats that are difficult to counter with present security methods.As a result,several safe communication protocols designed to enhance the secure interaction among MANET nodes.In this research,we offer a reputed optimal routing value among network nodes,secure computations,and misbehavior detection predicated on node’s trust levels with a Hybrid Trust based Reputation Mechanism(HTRM).In addition,the study designs a robust Public Key Infrastructure(PKI)system using the suggested trust evaluation method in terms of“key”generation,which is a crucial component of a PKI cryptosystem.We also concentrate on the solid node authenticating process that relies on pre-authentication.To ensure edge-to-edge security,we assess safe,trustworthy routes to secure computations and authenticate mobile nodes,incorporating uncertainty into the trust management solution.When compared to other protocols,our recommended approach performs better.Finally,we use simulations data and performance evaluation metrics to verify our suggested approach’s validity Our approach outperformed the competing systems in terms of overall end-to-end delay,packet delivery ratio,performance,power consumption,and key-computing time by 3.47%,3.152%,2.169%,and 3.527%,3.762%,significantly.

Research of Internet Trade Trust Based on the KMRW Reputation Model

The paper discusses the Internet trade trust problemby using KMRW reputation model in incompleteinformation limited repeated game,concluding thatin certain conditions,both Internet trade participatorswill give up short-time revenue for maximizinglong-term revenue and accordingly employ the cooperationstrategy,and the times of cooperation isvulnerable to trade price and product utility.At last,the paper proposes several policies and suggestionssuch as forming Internet honesty culture,strengtheningInternet brand and figure and protecting Internetwell-known brands.

A Reputation-Based Multi-Agent Model for Network Resource Selection

Because of the anonymity and openness of online transactions and the richness of network resources, the problems of the credibility of the online trading and the exact selection of network resources have become acute. For this reason, a reputation-based multi-agent model for network resource selection (RMNRS) is presented. The model divides the network into numbers of trust domains. Each domain has one domain-agent and several entity-agents. The model prevents the inconsistency of information that is maintained by differ-ent agents through the periodically communication between the agents. The model enables the consumers to receive responses from agents significantly quicker than that of traditional models, because the global reputation values of service providers and consumers are evaluated and updated dynamically after each transaction. And the model allocates two global reputation values to each entity and takes the recognition value that how much the service provider knows the service into account. In order to make users choose the best matching services and give users with trusted services, the model also takes the similarity between services into account and uses the similarity degree to amend the integration reputation value with harmonic-mean. Finally, the effectiveness and feasibility of this model is illustrated by the experiment.

基于匹配信任度机制的移动传感网簇头更新算法

为解决移动传感网部署过程中存在的簇头节点更新质量不佳和节点生存率较低等不足,提出了一种基于匹配信任度机制的移动传感网簇头更新算法。首先,引入K-means算法,利用误差平方根函数来完成网络初始聚类,以快速定位聚类中心,提升聚类形成速度。随后,综合考虑备选簇头剩余能量、备选簇头与当前簇头的欧氏距离、备选簇头覆盖范围内节点总数3个因素,设计了基于匹配信任度的簇头更新方法,按权值对各因素进行平均分配,进而将信任度权值最高的节点作为备选簇头,从而选举出生存质量较高的节点。仿真实验表明,算法具有更高的网络稳定运行时间和簇头节点生存率,以及更低的节点故障概率。其中,网络稳定运行时间提升了80%以上,簇头节点生存率保持在90%以上,节点故障概率也较低,具有明显的优势。

公共部门组织声誉何以影响公民政策遵从?——基于数字治理中公民信息采集意愿的调查实验研究

公民政策遵从关乎政策成败,治理现代化背景下亟须探索促进公民政策遵从的有效策略。公共部门组织声誉作为政策领域的“无形资产”,是否能够影响公民政策遵从意愿?当前学界尚未对以上问题做出回答。基于此,本文在已有研究基础上引入中介变量——政府信任和调节变量——组织声誉维度,构建公共部门组织声誉与公民政策遵从之间的关系模型,通过调查实验探究公共部门组织声誉与公民政策遵从之间的关系及其形成的内在机理。研究结果表明:其一,相较于带有负面声誉的公共部门,公众对享有正面声誉的公共部门表现出更高的遵从度;其二,组织声誉对公民政策遵从的影响并非仅限于直接产生,也可通过公民政府信任间接实现;其三,不同组织声誉维度下政府信任存在明显差异,说明组织声誉维度可作为一个调节政府可信度的机制。将公共部门组织声誉理论与公民政策遵从进行链接和对话,既丰富了公共部门组织声誉的微观研究,也为提升政府治理成效以及推进公民政策获得感提供了实践启示。

在线服务失败类型对消费者重购意愿的影响研究

为研究电商卖家在线服务失败类型对消费者重购意愿的影响,文章从消费者感知信任入手,针对两种情形下(电商卖家未达到平台规则vs未达到卖家承诺)开展了实证研究。结果表明,电商卖家相比较未达到卖家承诺,未达到平台规则,消费者对该卖家的感知信任更高,并且有更高的重购意愿。并且卖家声誉在上述关系中起调节作用。相比较高卖家声誉,电商卖家达到卖家承诺而未达到平台规则对消费者重购的负向影响程度在低卖家声誉的卖家中更加得以体现。

边缘计算环境下基于零信任的数字信誉评分模型设计

边缘计算通过在接近数据源的地方进行实时处理,与云计算相结合,提供高效能、低延迟的计算解决方案,构建了全面的分布式计算体系。然而,边缘计算的分布式和内部信任特性也导致了边界安全模糊的问题。零信任架构的出现打破了原先静态的边界防御模式,使设备在网络中能实现动态安全。但是,边缘设备的分布式特性导致边缘计算在零信任架构中存在动态管理的复杂性、资源限制等困难。针对此问题,本文提出一种基于零信任的数字信誉评分模型(DRSM),通过结合区块链,实现边缘计算环境下的动态访问管理和授权。该方案有助于解决边界安全模糊的问题,降低恶意攻击对系统造成的影响。仿真结果表明,本文模型能够有效减少异常边缘设备的访问,提高系统的安全性。

基于混合信任模型的DPoS共识机制改进研究

针对“端—边—云”架构中“局部可信,全局不可信”的混合信任模型,提出基于混合信任的共识方案RT-DPoS。首先,设计声誉机制规范节点的行为并增强节点的投票积极性;然后,提出基于局部信任的投票策略指导节点的投票行为,并根据投票行为建立局部信任关系,在此基础上,综合考虑节点的信任关系和声誉值,设计了两阶段的策略选出代理节点集合,增强了代理节点的去中心化;再在共识过程中引入可验证随机函数优化代理节点出块顺序,增强对腐败攻击的抵抗力。实验表明,RT-DPoS可以避免节点盲目投票,保证代理节点的可靠性和去中心化,并增强共识过程中的安全性,相较于DPoS,RT-DPoS提升了23.38%的安全性能。

RepTrust:P2P环境下基于声誉的信任模型

声誉是一种新型的信任建立机制,在P2P网络中,利用基于声誉的信任机制可以孤立劣质节点,选择更为可信的节点进行交互,以规避和减少风险。针对目前研究中存在的与网络结构耦合过紧、防止联合欺诈和声誉时效性处理不佳等问题,提出了一种新的基于声誉的信任模型RepTrust。模型采用与获取路径甚至评价者rater无关的声誉信息计算信任度,利用基于大多数原则的算法过滤不实声誉,利用衰减因子处理声誉的动态性。仿真实验表明模型在信任度计算、对付欺诈和动态声誉处理等方面效果明显。

基于更新质量检测和恶意客户端识别的联邦学习模型

作为分布式机器学习,联邦学习缓解了数据孤岛问题,其在不共享本地数据的情况下,仅在服务器和客户端之间传输模型参数,提高了训练数据的隐私性,但也因此使得联邦学习容易遭受恶意客户端的攻击。现有工作主要集中在拦截恶意客户端上传的更新。对此,研究了一种基于更新质量检测和恶意客户端识别的联邦学习模型umFL,以提升全局模型的训练表现和联邦学习的鲁棒性。具体而言,通过获取每一轮客户端训练的损失值来计算客户端更新质量,进行更新质量检测,选择每一轮参与训练的客户端子集,计算更新的本地模型与上一轮全局模型的相似度,从而判定客户端是否做出积极更新,并过滤掉负面更新。同时,引入beta分布函数更新客户端信誉值,将信誉值过低的客户端标记为恶意客户端,拒绝其参与随后的训练。利用卷积神经网络,分别测试了所提算法在MNIST和CIFAR10数据集上的有效性。实验结果表明,在20%~40%恶意客户端的攻击下,所提模型依旧是安全的,尤其是在40%恶意客户端环境下,其相比传统联邦学习在MNIST和CIFAR10上分别提升了40%和20%的模型测试精度,同时分别提升了25.6%和22.8%的模型收敛速度。

基于改进实用拜占庭容错的可信分布式区块链信任机制研究

针对区块链中实用拜占庭容错共识算法(Practical Byzantine Fault Tolerance,PBFT)中存在节点通信次数高、主节点选取简单、缺少奖惩机制及节点缺乏积极性等问题,提出了一种基于改进PBFT的可信分布式信任机制设计方案,即TM-PBFT(Trust Mechanism-PBFT)。首先,引入信誉模型对节点进行信誉值排序,增强对节点Sybil女巫攻击的防御能力,提升节点的积极性;其次,对节点进行分层共识,减少节点间通信开销;结合应用超级账本(Hyperledger Fabric)与星际文件系统(InterPlanetary File System,IPFS)对链上数据进行处理,减轻了数据的链上存储负担,提高了共识效率。在40个节点参与共识的条件下,通信次数相较于传统PBFT下降了85.8%,相较于C-PBFT算法下降了27.7%。

METrust: A Mutual Evaluation-based Trust Model for P2P Networks

It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer （P2P） network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers＇ evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers＇ malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers＇ own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.

A New Kind of Subjective Trust Model

Based on the outstanding characteristics of Cloud Model on the process of transforming a qualitative concept to a set of quantitative numerical values, a formalized model of subjective trust is introduced by which we can transform between qualitative reputation and quantitative voting data. The present paper brings forward algorithms to compute direct trust and recommender trust. Further more, an effective similarity measuring method used to distinguish two users＇ reputation on knowledge level is also proposed. The given model properly settles the uncertainty and fuzziness properties of subjective trust which is always the weakness of traditional subjective trust model, and provides a step in the direction of proper understanding and definition of human trust.

An Extended Hierarchical Trusted Model for Wireless Sensor Networks

Cryptography and authentication are traditional approach for providing network security. However, they are not sufficient for solving the problems which malicious nodes compromise whole wireless sensor network leading to invalid data transmission and wasting resource by using vicious behaviors. This paper puts forward an extended hierarchical trusted architecture for wireless sensor network, and establishes trusted congregations by three-tier framework. The method combines statistics, economics with encrypt mechanism for developing two trusted models which evaluate cluster head nodes and common sensor nodes respectively. The models form logical trusted-link from command node to common sensor nodes and guarantees the network can run in secure and reliable circumstance.

A Hierarchical Trust Model for Peer-to-Peer Networks

Trust has become an increasingly important issue given society’s growing reliance on electronic transactions.Peer-to-peer(P2P)networks are among the main electronic transaction environments affected by trust issues due to the freedom and anonymity of peers(users)and the inherent openness of these networks.A malicious peer can easily join a P2P network and abuse its peers and resources,resulting in a large-scale failure that might shut down the entire network.Therefore,a plethora of researchers have proposed trust management systems to mitigate the impact of the problem.However,due to the problem’s scale and complexity,more research is necessary.The algorithm proposed here,HierarchTrust,attempts to create a more reliable environment in which the selection of a peer provider of a file or other resource is based on several trust values represented in hierarchical form.The values at the top of the hierarchical form are more trusted than those at the lower end of the hierarchy.Trust,in HierarchTrust,is generally calculated based on the standard deviation.Evaluation via simulation showed that HierarchTrust produced a better success rate than the well-established EigenTrust algorithm.

A Trust Model Based on the Multinomial Subjective Logic for P2P Network

In order to deal with the problems in P2P systems such as unreliability of the Service, security risk and attacks caused by malicious peers, a novel trust model MSL-TM based on the Multinomial Subjective Logic is proposed. The model uses multinomial ratings and Dirichlet distribution to compute the expectation of the subjective opinion and accordingly draws the peer’s reputation value and risk value, and finally gets the trust value. The decay of time, rating credibility and the risk value are introduced to reflect the recent behaviors of the peers and make the system more sensitive to malicious acts. Finally, the effectiveness and feasibility of the model is illustrated by the simulation experiment designed with peersim.

Trust Type Based Trust Bootstrapping Model of Computer Network Collaborative Defense

In the system of Computer Network Collaborative Defense(CNCD),it is difficult to evaluate the trustworthiness of defense agents which are newly added to the system,since they lack historical interaction for trust evaluation.This will lead that the newly added agents could not get reasonable initial trustworthiness,and affect the whole process of trust evaluation.To solve this problem in CNCD,a trust type based trust bootstrapping model was introduced in this research.First,the division of trust type,trust utility and defense cost were discussed.Then the constraints of defense tasks were analyzed based on game theory.According to the constraints obtained,the trust type of defense agents was identified and the initial trustworthiness was assigned to defense agents.The simulated experiment shows that the methods proposed have lower failure rate of defense tasks and better adaptability in the respect of defense task execution.

Trusted Heartbeat Framework for Cloud Computing

In cloud computing environment, as the infrastructure not owned by users, it is desirable that its security and integrity must be protected and verified time to time. In Hadoop based scalable computing setup, malfunctioning nodes generate wrong output during the run time. To detect such nodes, we create collaborative network between worker node (i.e. data node of Hadoop) and Master node (i.e. name node of Hadoop) with the help of trusted heartbeat framework (THF). We propose procedures to register node and to alter status of node based on reputation provided by other co-worker nodes.