An incentive compatible reputation mechanism for ubiquitous computing environments

The vision of ubiquitous computing is becoming a reality thanks to the advent of portable devices and the advances in wireless networking technologies. It aims to facilitate user tasks through seamless utilization of services available in the surrounding environments. In such distributed environments featuring openness, interactions such as service provision and consumption between entities that are unknown or barely known to each other, are commonplace. Trust management through reputation mechanism for facilitating such interactions is recognized as an important element of ubiquitous computing. It is, however, faced by the problems of how to stimulate reputation information sharing and enforce honest recommendation elicitation. We present in this paper an incentive compatible reputation mechanism to facilitate the trustworthiness evaluation of entities in ubiquitous computing environments. It is based on probability theory and supports reputation evolution and propagation. Our reputation mechanism not only shows robustness against lies, but also stimulates honest and active recommendations. The latter is realized by ensuring that active and honest recommenders, compared to inactive or dishonest ones, can obtain the most number of honest (helpful) recommendations and thus suffer the least number of wrong trust decisions, as validated by simulation based evaluation. The proposed reputation mechanism is also implemented as part of a QoS-aware Web service discovery middleware and evaluated regarding its overhead on service discovery latency.     

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.     

P2P网络激励兼容的声誉评价及服务分配

由自私节点组成的P2P网络常常采用基于声誉的机制激励节点间的合作。当前已经提出了一类基于声誉的方案,但其中大部分方案都忽视了方案的分布式实现中惩罚的激励兼容性问题。在社会学或生态学领域,该问题常被称为二阶搭便车问题:拒绝低声誉节点的服务请求会降低惩罚者自身的声誉,因此对于自私节点而言,这种惩罚并不总是有利的选择。以P2P文件共享为例对该问题进行了分析,提出了一种连续型二阶声誉评价模型及两种服务分配规则,即概率分配规则和比例分配规则,实验表明只有概率分配规则是激励兼容的。     

RRM: An incentive reputation model for promoting good behaviors in distributed systems

Reputation systems represent soft security mechanisms that complement traditional information security mechanisms. They are now widely used in online e-commerce markets and communities in order to stimulate good behaviors as well as to restrain adverse behaviors. This paper analyzes the limitations of the conversational reputation models and proposes an incentive reputation model called the resilient reputation model （RRM） for the distributed reputation systems. The objective of this reputation model is not only to encourage the users to provide good services and, therefore, to maximize the probability of good transaction outcomes, but also to punish those adverse users who are trying to manipulate the application systems. The simulation results indicate that the proposed reputation model （RRM） could effectively resist against the common adverse behaviors, while protecting the profits of sincere users from being blemished by those adversaries.     

基于声誉的移动众包系统的在线激励机制

在大数据环境下,对移动众包系统的研究已经成为移动社会网络（MSN）的研究热点。然而由于网络个体的自私性,容易导致移动众包系统的不可信问题,为了激励个体对可信策略的选取,提出一种基于声誉的移动众包系统的激励机制——RMI。首先,结合演化博弈理论和生物学中的Wright-Fisher模型研究移动众包系统的可信演化趋势;在此基础上,分别针对free-riding问题和false-reporting问题建立相应的声誉更新方法,从而形成一套完整的激励机制,激励感知用户和任务请求者对可信策略的选取;最后通过模拟实验对提出的激励机制的有效性和适应性进行了验证。结果显示,与传统的基于社会规范的声誉更新方法相比,RMI有效地提高了移动众包系统的可信性。     

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.     

P2P网络应用动态规划算法控制搭便车行为的研究

针对P2P网络中节点共享存储资源时存在的“搭便车”问题,在节点存储资源分配时引入控制机制,节点根据请求者的贡献值分配其存储资源,以使贡献值小的节点获取资源少,从而抑制搭便车行为。仿真实验表明,该控制机制的动态规划算法能有效地实现P2P网络中存储资源分配的公平性,达到控制节点搭便车行为的目的。     

对等网络信任和信誉机制研究综述

介绍了对等网中的信任和信誉机制所涉及的概念与研究内容，对其近年来的研究进展进行了归纳分析，提出了对等网络的中信任和信誉机制研究中涉及的问题,并针对该系统设计中涉及的关键问题提出了相应的思路和研究方法。     

P2P soft security: On evolutionary dynamics of P2P incentive mechanism

This paper thoroughly investigates the evolutionary dynamics of soft security mechanism, namely, reciprocity-based incentive mechanism, in P2P systems based on Evolutionary Game Theory (EGT). By soft security mechanism, it means social control mechanisms to overcome peers’ selfish (rational) behaviors, and encourage cooperation in P2P systems. Specifically, there exist three strategies in P2P systems: always cooperative (ALLC), always defect (ALLD) and reciprocator (R). Instead of existing work which take it for granted that, like ALLC users, R users did not bear any information-seeking cost, we assume small reciprocation cost, and study generalized mutation-selection dynamics. Our contributions are threefold: firstly, we prove and illustrate that, in a well-mixed P2P structure, ALLD is the only strict Nash equilibrium; secondly, we infer the specific condition under which evolution dynamics exhibits rock-scissors-paper oscillation in a structured P2P population. That is, the population cycles from ALLD to R to ALLC and back to ALLD; finally, we theoretically illustrate that the intensity of selection plays an important role in the evolutionary dynamics of P2P incentive mechanism. That is, when the intensity of selection is relatively weak and reciprocation cost limits to zero, the time average can be mostly concentrated on reciprocator. In brief, considering the existence of reciprocation cost and the small mutation in P2P incentive mechanisms, unlike existing work, it is impossible to simply achieve the “absolute cooperative” in P2P incentive mechanisms. On the other hand, stochastic evolution in P2P incentive mechanism with finite population and network structure still favor reciprocation.     

P2P环境下引入激励机制的动态信任模型*

针对现有的P2P信任模型中对节点行为改变的动态适应能力不足以及欠缺激励机制的特点,提出了一种引入激励机制的动态信任模型（DITrust）。该模型采用信任值动态衰减策略以提高其动态适应能力,同时通过推荐偏移量来计算推荐信誉值,增加了推荐信誉的可信度;最后引入虚拟信誉货币以鼓励节点提供服务和推荐信息,提高了节点模型参与的主动性。实验结果表明,DITrust较已有的模型能够抵御类型更广泛的恶意节点攻击,且具有更小的网络开销。     

移动P2P网络基于博弈论方法的协作激励机制

在移动P2P网络中,部分自私的移动节点只是大量地消耗已有网络资源而不提供自己的资源。考虑到移动P2P网络的资源有限性以及部分节点具有自私行为的特点,提出了一种基于博弈论的协作激励机制。该机制根据移动节点的不同贡献大小来提供不同网络服务质量,最大限度地鼓励了每个移动节点参与协作和共享。同时,详细描述和分析了协作激励机制中的网络资源分配策略,并且证明了该协作激励机制的博弈存在一个稳定的纳什（Nash）均衡。通过仿真实验发现,该激励机制有效地激发了移动节点间的协作,优化了整个网络的性能。同现有的协作激励策略相比,提高了数据包转发率等。     

Optimal incentive compatible mechanism in a system with several active elements

This paper considers the optimal mechanism design problems in an active system composed of a Principal and several active elements (AEs) with a shared incentive fund under the Principal’s incomplete awareness about the model parameters of the AEs. We find the optimal mechanisms that include the planning procedures, the penalty and incentive functions, as well as the incentive fund calculation and allocation procedures under which the AEs are interested in truth-telling and plan fulfillment.     

A simulation framework for measuring robustness of incentive mechanisms and its implementation in reputation systems

In game theoretical analysis of incentive mechanisms, all players are assumed to be rational. Since it is likely that mechanism participants in the real world may not be fully rational, such mechanisms may not work as effectively as in the idealized settings for which they were designed. Therefore, it is important to evaluate the robustness of incentive mechanisms against various types of agents with bounded rational behaviors. Such evaluations would provide us with the information needed to choose mechanisms with desired properties in real environments. In this article, we first propose a general robustness measure, inspired by research in evolutionary game theory, as the maximal percentage of invaders taking non-equilibrium strategies such that the agents sustain the desired equilibrium strategy. We then propose a simulation framework based on evolutionary dynamics to empirically evaluate the equilibrium robustness. The proposed simulation framework is validated by comparing the simulated results with the analytical predictions based on a modified simplex analysis approach. Finally, we implement the proposed simulation framework for evaluating the robustness of incentive mechanisms in reputation systems for electronic marketplaces. The results from the implementation show that the evaluated mechanisms have high robustness against a certain non-equilibrium strategy, but is vulnerable to another strategy, indicating the need for designing more robust incentive mechanisms for reputation management in e-marketplaces.     

基于等级结构的对等网络激励机制研究

搭便车行为对对等网络造成严重负面影响。首先提出了一种基于等级概念的网络激励机制以抑制搭便车行为并解决公共悲剧问题。所提出的效用函数为公平性特别考虑了用户的绝对贡献值和物理特性,并根据层次分析法来计算它们的值。通过实验仿真证明了此种机制的有效性和实用性,并对此机制的发展给出了展望。     

An incentive scheduling mechanism for peer-to-peer video streaming

P2P video streaming networks are found as a scalable solution and an alternative for traditional client–server based video streaming over the Internet. One of the significant issues affecting the success of any P2P streaming network is cooperation between peers. Practical observations have proved the prevalence of free riders in P2P networks that degrade their performance. To solve this problem, using incentive mechanisms, which encourage peers to contribute more in the network, is necessary. In this paper, we designed and proposed a distributed and scalable incentive mechanism for mesh based P2P video streaming networks. In the proposed approach the contribution of the peers is measured and maintained in a distributed fashion. Furthermore, we proposed an incentive sending side scheduler in which peers are served based on their contribution in the network. Our simulation evaluations show the efficiency of the proposed approach in improving the overall perceived video quality by the non-free rider nodes and consequently in the whole network.     

一种P2P网络安全的信誉度模型设计

通过建立一种信誉度模型来隔离恶意节点,保证网络安全可靠并能够提供更好质量、更加有效的服务。从信誉角度构造了一个安全的P2P网络,给出了求解信誉度的数学模型和具体算法以及决定信誉度的一些因素,并进行了实验模拟分析,验证此信誉度模型能够有效隔离恶意节点。     

A dynamic load balancing scheme with incentive mechanism in heterogeneous structured P2P networks

Dynamic load imbalance is a basic and inherent problem in structured P2P networks. Most existing research suffers from the problems of inefficiency in globally managing the nodes’ load information and consumption of network bandwidth. This paper describes the mechanisms for collecting and globally managing the dynamic load of each node, and based on which to present a load balancing strategy which transfers the load from overloaded to under loaded nodes so as to improve load balancing efficiency. In order to encourage the rational and selfish nodes to actively participate in the load balancing process, we also propose an incentive mechanism in dynamic load balancing, by which the differentiated services could be provided for the nodes according to their load balancing abilities. The simulation results indicate that our approach could tackle the load imbalance problem in structured P2P networks effectively and efficiently in terms of the load distribution and the transferred load volume.     

An incentive mechanism designed for e-marketplaces with limited inventory

In electronic marketplaces, reputation systems and incentive mechanisms are prevalently employed to promote the honesty of sellers and buyers. In this article, we focus on the scenario in which the inventory is in short supply, i.e. an e-marketplace with limited inventory (EMLI). The challenges are in twofold: (a) for sellers who aim to maximize their profit, they may intentionally conduct dishonest transactions since the limited products are likely to be sold out regardless of their reputation; (b) for buyers who intend to gain the limited products, they may provide untruthful ratings to mislead other buyers. To address these issues, we propose an incentive mechanism to promote buyer and seller honesty for this type of e-marketplaces. Specifically, the mechanism models the honesty of buyers and sellers as scores and reputation, respectively. It then offers a higher price to the products of more honest sellers (with higher reputation) and allocates the products to more honest buyers (with higher scores). In this way, both sellers and buyers are well encouraged to be honest. Furthermore, we impose proper membership fee on new sellers to cope with the whitewashing attack. We finally theoretically analyze and empirically demonstrate the efficacy of the proposed mechanism and its nice properties.     

Trusted P2P transactions with fuzzy reputation aggregation

Internet commerce and online commodity exchanges suffer from distrust among sellers and buyers, who are often strangers to each other. The authors present a new P2P reputation system based on fuzzy logic inferences, which can better handle uncertainty, fuzziness, and incomplete information in peer trust reports. This system aggregates peer reputations with affordable message overhead. By testing the system using eBay transaction data in the public domain, the authors seek to demonstrate the efficacy and robustness of two P2P reputation systems - fuzzytrust and eigentrust - at establishing trust among the peers in P2P applications.     

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.