An incentive mechanism to reinforce truthful reports in reputation systems

The success of current trust and reputation systems is on the premise that the truthful feedbacks are obtained. However, without appropriate mechanisms, in most reputation systems, silent and lying strategies usually yield higher payoffs for peers than truthful feedback strategies. Thus, to ensure trustworthiness, incentive mechanisms are highly needed for a reputation system to encourage rational peers to provide truthful feedbacks. In this paper, we model the feedback reporting process in a reputation system as a reporting game. We propose a wage-based incentive mechanism for enforcing truthful report for non-verifiable information in self-interested P2P networks. A set of incentive compatibility constraint rules including participation constraint and self-selection constraints are formulated. We design, implement, and analyze incentive mechanisms and players’ strategies. The extensive simulation results demonstrate that the proposed incentive mechanisms reinforce truthful feedbacks and achieve optimal welfare.     

一个非结构化P2P网络中基于信誉的激励机制

在P2P网络中,freerider节点和恶意节点的不合作行为严重影响了P2P服务的可用性。为此,本文提出了一个应用于非结构化P2P环境下基于信誉的激励机制。该机制通过有限的信任信息的共享,实现信任评估和推荐信任度评估;并以此为基础,通过有效的拓扑构造和服务选择策略及服务请求冲突解析策略的选择,实现对合作节点的激励和对freerider和恶意节点的遏制及惩罚。分析及仿真实验表明,本文提出的基于信誉的激励机制是简单有效的。     

基于信任的P2P拓扑进化机制

现有的非结构Peer-to-Peer(P2P)系统缺乏对拓扑公平性的考虑,并且不能对某些节点的恶意行为进行有效的抑制。其主要原因在于构造的拓扑对节点信任度的不敏感性,忽略了P2P网络中各节点的异构性。据此,首先给出了基于反馈可信度的节点全局信任度计算模型,然后在此基础上提出了一种针对非结构化P2P网络的自适应拓扑进化机制。利用该机制,可使高可信节点占据拓扑的有利位置,低可信节点处于不利位置,从而体现拓扑的公平性。该机制同时能够对节点的恶意行为进行有效的抑制,并具有激励性质,鼓励节点提供更好的服务,以获得更高的响应率。分析和仿真结果表明,该机制较之现有机制,在拓扑的有效性和激励性上有较大的提高。     

An incentive compatible reputation mechanism for P2P systems

In peer-to-peer (P2P) systems, peers often must interact with unknown or unfamiliar peers without the benefit of trusted third parties or authorities to mediate the interactions. Trust management through reputation mechanism to facilitate such interactions is recognized as an important element of P2P systems. It is, however, faced by the problems of how to stimulate reputation information sharing and honest recommendation elicitation. This paper presents an incentive compatible reputation mechanism for P2P systems. It has two unique features: (1) a recommender’s trustworthiness and level of confidence about the recommendation is considered for a more accurate calculation of reputations and fair evaluation of recommendations. (2) Incentive for participation and honest recommendation is implemented through a fair differential service mechanism. It relies on peer’s level of participation and on the recommendation credibility. Theoretic analysis and simulation show that the reputation mechanism we propose can help peers effectively detect dishonest recommendations in a variety of scenarios where more complex malicious strategies are introduced. Moreover, it can also stimulate peers to send sufficiently honest recommendations. The latter is realized by ensuring that active and honest recommenders, compared to inactive or dishonest ones, can elicit the most honest (helpful) recommendations and thus suffer the least number of wrong trust decisions.     

An adaptive peer-to-peer live streaming system with incentives for resilience

Recently, there have been a lot of research efforts on peer-to-peer (P2P) live streaming services. P2P systems can be easily deployed since a participating peer’s resources (i.e., upload link bandwidth) can be exploited to distribute contents. However, how to adapt to leaving peers and how to encourage peers to contribute resources voluntarily are still challenging issues. In this paper, we propose Climber, an adaptive P2P live streaming system with incentives for resilience. Climber is based on the hybrid structure of a tree and a mesh, so as to achieve self-improvement and adaptation to users’ dynamic joining and leaving. Moreover, Climber substantiates an incentive mechanism that provides better resilience for peers with more upload bandwidth allocated. Simulation results reveal that Climber significantly reduces the topology maintenance cost compared to SplitStream and NICE-PRM. Also, simulation and analytical results verify that Climber can bound the level of disruption by dynamically adapting to the user churning rate.     

Reputation based probabilistic resource allocation for avoiding free riding and formation of common interest groups in unstructured P2P networks

Free riding is a major problem in peer-to-peer networks. Reputation management systems are generally employed to overcome this problem. In this paper, a new reputation based scheme called probabilistic resource allocation is proposed. This strategy probabilistically decide whether to provide the resource to requesting peer or not. Aforesaid method gives selection preference to higher reputation peers and at the same time provides some finite probability of interaction between those peers who don’t have good reputation about each other. This avoids disconnection between the aforesaid peers. The proposed scheme also introduces a new mechanism for resource distribution which not only allocates resources based on peers’ reputation but simultaneously maximizes network utility also. Algorithm for formation of interest groups based upon both similarity of interests and reputation between peers is also presented.     

An incentive mechanism for message relaying in unstructured peer-to-peer systems

Distributed message relaying is an important function of a peer-to-peer system to discover service providers. Existing search protocols in unstructured peer-to-peer systems create huge burden on communications, cause long response time, or result in unreliable performance. Moreover, with self-interested peers, these systems are vulnerable to the free-riding problem. In this paper we present an incentive mechanism that not only mitigates the free-riding problem, but also achieves good system efficiency in message relaying for peer discovery. In this mechanism promised rewards are passed along the message propagation process. A peer is rewarded if a service provider is found via a relaying path that includes this peer. The mechanism allows peers to rationally trade-off communication efficiency and reliability while maintaining information locality. We provide some analytic insights to the symmetric Nash equilibrium strategies of this game, and an approximate approach to calculate this equilibrium. Experiments show that this incentive mechanism brings a system utility generally higher than breadth-first search and random walks, based on both the estimated utility from our approximate equilibrium and the utility generated from learning in the incentive mechanism.     

EMMP: a highly efficient membership management protocol

Gossip (or epidemic) algorithms have recently become popular solutions to multicast message dissemination in peer-to-peer systems. Nevertheless, it is not straightforward to apply gossip to on-demand streaming because it often fails to achieve a timely delivery. To solve this problem and taking into account the characteristic of peers randomly joining and leaving in peer-to-peer systems, an Efficient Membership Management Protocol (EMMP) has been presented. Every node only needs to keep contact with O (log(N)) nodes, and EMMP can support the reliable dissemination of messages. Considering the “distance” between peers, it causes the major data to be transmitted in a local area and reduces the backbone’s traffic, and speeds up the dissemination of messages between peers. This paper has adopted the “goodfriend” mechanism to reduce the influence on the system when a peer fails or leaves. Simulation results show that EMMP is highly efficient, and both the redundancy and the delay of the system are well solved.     

一种基于反馈可信度的分布式P2P信任模型

开放、共享与匿名的Peer-to-Peer(简称P2P)网络已经取得了越来越多的应用,无中心对等的特性也吸引了越来越多的用户.但由于其网络中的节点不受约束,资源的共享是用户自愿的行为,因此节点间的信任很难通过传统的信任机制建立.一种可行的解决方案是借鉴人际网络中的信任关系,建立一种基于信誉的全局信任模型.已有的工作基本建立在信任度高的节点其反馈也更可信这个假设的基础上,将节点的反馈质量简单地等同于服务质量.针对这一问题,提出了一种基于节点反馈可信度的分布式P2P全局信任模型(简称FCTrust),用于量化和评估节点的可信程度,并给出了模型的数学表述和分布式实现方法.分析及仿真实验结果表明,FCTrust较已有的全局信任模型在遏制更广泛类型的恶意节点攻击的有效性、迭代计算的收敛性及消息成本上有较大提高.     

一种节点信誉相关的P2P网络信任管理模型

现有的P2P系统中存在大量的欺诈行为和不可靠的服务.本文通过模拟社会关系网络中信任的形成机制,提出P2P网络信任管理模型TMMRN,TMMRN通过考察节点的信誉值来进行安全交易,节点的信誉主要来自于其他节点对它的加权信任反馈,在信誉计算中增加了激励机制.TMMRN还可减少交易时的网络负担.实验表明TMMRN可提高信誉值的计算效率,能有效抵抗恶意节点的攻击,还可激励懒惰节点主动参与到系统中来.     

Stochastic Optimization for Content Sharing in P2P Systems

Available resources in peer-to-peer (P2P) systems depend strongly on resource contributions made by individual peers. Empirical data shows that in the absence of incentives, a majority of the participating peers do not contribute resources. Modeling interactions between individual peers is often difficult as the number of peers in the system can be very large, and the relationships among them can be very complex. In this paper, we propose a new solution for P2P systems, where peers upload and download content to and from the contributing peers based on agreed-upon/determined sharing rates. We propose a P2P solution that deters free-riders by imposing constraints on participating peers; specifically, a peer is allowed access to new content only as long as its own content contribution exceeds an adaptively set threshold. The constraints are enforced either by a central authority (e.g., a tracker) or by a decentralized coalition of peers in a swarm, social network, etc. We derive optimal upload policies for the peers given their estimated future download requirements and their previous contribution (credit) to the other peers. Our results show considerable improvement in the cost-benefit tradeoff for peers that deploy such an optimal policy as compared to heuristic upload policies. We also propose mechanisms based on which the coalition of peers can provide incentives or penalties to participating peers to adjust their policies such that the availability of content and/or number of peers contributing content is maximized.     

基于反馈相关性的P2P网络信任模型

用户对P2P网络安全性的需求刺激了信任模型的发展。在分析现有信任模型的基础上,提出了基于反馈相关性的动态信任模型—CoDyTrust。其在时间帧的基础上,采用虚假信任过滤机制和信任聚合机制,并在信任值计算中引入信任相关系数、信任遗忘因子、滥用信任值和推荐信任度等,通过反馈控制机制动态调节这些模型因子,在准确评价节点对不同资源信任的同时,实现网络中恶意行为检测。比较分析结果表明,CoDyTrust能够更好地反映网络中节点行为,准确检测恶意节点,有效抵御振荡、撒谎和合谋等攻击。     

一种新的P2P系统中基于双ratings的声誉管理机制

声誉系统的效率取决于回馈的质量,现有P2P系统中的声誉模型不能处理合谋、不递交ratings等攻击,并且对无过失节点还存在不公平性,针对这些问题,提出了一个新的基于双ratings的声誉管理机制,采用两个度量来评价节点:服务信任和回馈信任,服务信任反映server提供服务的可靠性;回馈信任反映consumer汇报ratings的可信度.一次交易后,要求服务双方都递交ratings,依据这两个ratings对server的服务信任和consumer的回馈信任进行更新,并且server的服务信任更新与consumer的回馈信任紧密相关.为了防止恶意节点反复实施恶意行为或不递交rating,还提出了一个惩罚机制,作为对声誉模型的补充,仿真表明,提出的声誉管理机制能够抵制上述恶意攻击,减少不公平性.     

一种细粒度的基于灰色关联度的P2P信任模型①

已有的P2P网络信任模型过于粗糙,对反馈评价进行综合的能力不足。针对这一问题,提出了一种细粒度的基于灰色关联度的P2P信任模型GM—TRUST,根据节点的兴趣和专长将节点化分为不同的域,通过对具体服务各属性评价的综合得出直接信任。引入记忆因子来刻画信任随时间衰减的特性,并利用基于灰色相关度的方法来量化推荐信任的准确度。分析与实验均表明本模型与以往的信任模型相比,能够更准确地评估出节点的信任值,对动态恶意节点和不诚实反馈节点的攻击具有很好的抑制能力。     

Score-based Incentive Mechanism (SIM) for live multimedia streaming in peer-to-peer network

A decentralized model is primarily used for communication and file transfer, the peer-to-peer network is based on this model. The field of real-time communication and media streaming has witnessed enormous growth in recent times owing to their use of peer-to-peer network. A significant part of Internet traffic is being created by the peer-to-peer network resulting in an increase in its demand. The rise in prominence of peer-to-peer network can be attributed to its properties like resource utilization and distributed nature. Resource utilization of peer-to-peer network is one foremost motive for selecting this network over the traditional client-server architecture. The availability of selfish peer or free riders affects the total resource utilization and degrades the performance of the network. So, there is a requirement of an incentive-based mechanism to motivate the selfish peer or free riders in the network to improve the performance of the network. In this paper, we will discuss a new score based incentive mechanism to improve the overall resource utilization. This approach is based on a reward-punishment based method, so the peers are encouraged or motivated to share more resources and chastise selfish peers. For calculating the score value of a peer, parameters like upload capacity, video quality, control packets, a time period for which peers stay in the network can be used. Different score values are assigned for different parameters. The simulation results presented in this paper verify the approach and illustrate that the quality of video and performance of network improves using our new score based incentive mechanism. The parameters used for calculating the performances are; end-to-end delay, playback delay, start-up delay, and frame redundancy etc.

     

面向半分布式P2P系统的可靠节点交换机制

P2P系统中广泛使用的来源交换(PEX)技术为系统用户提供了丰富的节点资源,加快了节点交换速度,同时也带来了不可避免的安全隐患,恶意节点可以通过来源交换对正常节点的邻居列表进行污染。首先分析了现有来源交换技术存在的安全隐患,讨论了导致这些安全隐患的核心因素;其次,以来源交换的安全性分析为基础,提出了一种面向半分布式P2P系统的安全节点交换机制。该机制借鉴基于信任评估的激励技术,通过给出超级节点的信任评估方案,引入节点来源信任的概念,以节点的来源交换信任值为支撑,控制节点间的来源交换。最后,对提出机制进行了实验性能分析。实验结果表明,由于网络异构性所存在的信任值错误计算问题,所提出的交换机制虽然会导致约2.5%比例正常的节点交换失效,但可大幅度降低正常节点参与恶意节点传播和污染的情况,从而在总体上提升了P2P系统的可靠性。     

SocialTrust: Enabling long-term social cooperation in peer-to-peer services

Peer-to-peer(P2P) services heavily rely on users’ cooperation to achieve desired performance. However, most current P2P systems only encourage short-term and direct cooperation between peers. The lack of incentives for long term and indirect cooperation has severely limited the performance of P2P systems. On the other hand, recent measurements on large-scale networks show that peers’ behavior often demonstrates strong social patterns. In this paper, we design and implement a social P2P network, named SocialTrust, based on peers’ common interests. In SocialTrust, each peer tries to find a small number of friends and maintains long term social links with them. We also propose a distributed trust mechanism. The trust between two friends reflects their cooperation level and serves as the credit limit between them. A peer with higher trust can download data from its friends more efficiently. The trust can be propagated among friends to support indirect reciprocity. We formally prove that the proposed distributed trust mechanism is secure and can defend against various forms of attacks. By adding asmall number of long term social links to the existing P2P network, SocialTrust relaxes the constraint of direct incentive mechanisms and encourages peers to perform various forms of long-term cooperation. Both trace-driven simulation and real Internet experiments show that SocialTrust can significantly improve file availability and download performance of current P2P file sharing systems.     

P2P系统的信任研究

由于P2P系统的开放、匿名等特点,使得P2P系统对节点缺乏约束机制,节点间缺乏信任。针对以上问题,本文提出了一种新的P2P系统信任模型,该模型根据系统中节点的历史交易情况和系统中其它节点的推荐计算节点的信任度,节点根据计算的结果决定是否进行交易。仿真试验及分析表明,该模型能有效地评估节点的信任度,隔离恶意节点,提高下载成功率。     

On incentivizing upload capacity in P2P-VoD systems: Design,analysis and evaluation

Free riding has long been a serious problem in peer-to-peer (P2P) systems due to the selfish behavior of individual users. To conquer this problem, a key design issue of the P2P systems is to appropriately incentivize users to contribute resources. In P2P Video-on-Demand (VoD) applications, content providers need to incentivize the peers to dedicate bandwidth and upload data to one other so as to alleviate the upload workload of their content servers. In this paper, we design a simple yet practical incentive mechanism that rewards each peer based on its dedicated upload bandwidth. We use a mean field interaction model to characterize the distribution of number of peers in different video segments, based on which we characterize the content providers’ uploading cost as a function of the peers’ contribution. By using a game theoretic framework, we analyze the interaction between a content provider’s rewarding strategy and the peers’ contributing behaviors and derive a unique Stackelberg equilibrium. We further analyze the system efficiency in terms of the price of anarchy and study the long term behavior of the system under a repeated game setting. Via extensive simulations, we validate the stability and efficiency of our incentive scheme.     

一种激励相容的P2P信誉模型

针对现有的信任模型不能很好地处理P2P网络环境中恶意节点提供虚假服务的欺作行为,及不积极提供诚实推荐的问题,提出了一种激励相容的P2P信誉模型(简称ICRM)。该模型使用时间区间的概念来标示经验和推荐的时间特性,利用直接信任度、推荐信任度及推荐可信度等机制来精确描述节点的实际信任等级,并引入参与层次来度量节点提供推荐的积极程度,从而有效地识别与抑制不同类型的恶意节点,激励节点积极提供诚实推荐。仿真实验表明,ICRM能够有效地抑制恶意节点的欺作行为及不诚实反馈行为,并能有效解决节点推荐积极性不高的问题。