Fuzzy computational models for trust and reputation systems

In the recent past, a considerable research has been devoted to trust and reputation mechanisms to simplify complex transactions for open environments in social networking, e-commerce, and recommender systems (RS). In real life, we come to know about others through our social circle according to their reputation which is a public view. However, it is not always adequate to depend solely on the public view and therefore a trust measure is required to give a personalized view of the future encounters with a specific partner. In this paper, we propose fuzzy computational models for both trust and reputation concepts. Reciprocity and experience are used for trust modeling while the proposed reputation model is a fuzzy extension of beta reputation model. A two-level filtering methodology is proposed to benefit to a large extent from both the concepts separately. In order to justify the proposed models, we compared them with the existing reputation models for movie RS. The experimental results show that the incorporation of trust and reputation concepts into RS indeed improves the recommendation accuracy and establish that our models are better than beta and the popular eBay reputation models.     

基于信任和推荐的P2P信誉模型

分析现有信誉模型,提出一种使用信任机制和推荐机制的P2P信誉模型,利用决策树思想优化该模型。给出一种在分布式P2P系统中存取全局信任值的方法,解决了单点失效问题。实验结果表明,该模型对信任值的估算准确度以及判别虚假节点的能力高于现有模型。     

虚拟计算环境中一种基于惩罚激励的信誉模型

基于信誉构建信任机制是解决虚拟计算环境[1]中行为可信问题的重要途径。现有信誉模型对不良行为的动态适应能力和对信誉信息的有效聚合能力不足,缺乏鼓励节点积极参与诚实合作的有效机制。本文提出一种基于惩罚激励机制的信誉管理模型PERep,根据自主元素行为特征和信誉状态区分交易行为的合作与偏离,并对偏离行为进行惩罚;给出了PERep的分布式实现。实验表明,PERep能准确地区分诚实交易和恶意行为,有效提高自主元素诚实交易的积极性并减少恶意行为的危害。     

Securing decentralized reputation management using TrustGuard

Reputation systems have been popular in estimating the trustworthiness and predicting the future behavior of nodes in a large-scale distributed system where nodes may transact with one another without prior knowledge or experience. One of the fundamental challenges in distributed reputation management is to understand vulnerabilities and develop mechanisms that can minimize the potential damages to a system by malicious nodes. In this paper, we identify three vulnerabilities that are detrimental to decentralized reputation management and propose TrustGuard—a safeguard framework for providing a highly dependable and yet efficient reputation system. First, we provide a dependable trust model and a set of formal methods to handle strategic malicious nodes that continuously change their behavior to gain unfair advantages in the system. Second, a transaction-based reputation system must cope with the vulnerability that malicious nodes may misuse the system by flooding feedbacks with fake transactions. Third, but not the least, we identify the importance of filtering out dishonest feedbacks when computing reputation-based trust of a node, including the feedbacks filed by malicious nodes through collusion. Our experiments show that, comparing with existing reputation systems, our framework is highly dependable and effective in countering malicious nodes regarding strategic oscillating behavior, flooding malevolent feedbacks with fake transactions, and dishonest feedbacks.     

Magentix2: A privacy-enhancing Agent Platform

Agent Platforms are the software that supports the development and execution of Multi-agent Systems. There are many Agent Platforms developed by the agent community, but they hardly consider privacy. This leads to agent-based applications that invade users’ privacy. Privacy can be threatened by two main information activities: information collection and information processing. Information collection can be prevented using traditional security mechanisms. Information processing can be prevented by minimizing data identifiability, i.e., the degree by which personal information can be directly attributed to a particular individual. However, minimizing data identifiability may directly affect other crucial issues in Multi-agent Systems, such as accountability, trust, and reputation. In this paper, we present the support that the Magentix2 Agent Platform provides for preserving privacy. Specifically, it provides mechanisms to avoid information collection and information processing when they are not desired. Moreover, Magentix2 provides these mechanisms without compromising accountability, trust, and reputation. We also provide in this paper an application built on top of Magentix2 that exploits its support for preserving privacy. Finally, we provide an extensive evaluation of the support that Magentix2 provides for preserving privacy based on that application. We specifically test whether or not privacy loss can be minimized by using the support that Magentix2 provides, whether or not this support introduces a bearable performance overhead, and whether or not existing trust and reputation models can be implemented on top of Magentix2.     

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。     

An integrated trust and reputation model for open multi-agent systems

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent’s likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent’s environment.     

TREET: the Trust and Reputation Experimentation and Evaluation Testbed

To date, trust and reputation systems have often been evaluated using methods of their designers’ own devising. Recently, we demonstrated that a number of noteworthy trust and reputation systems could be readily defeated, revealing limitations in their original evaluations. Efforts in the trust and reputation community to develop a testbed have yielded a successful competition platform, ART. This testbed, however, is less suited to general experimentation and evaluation of individual trust and reputation technologies. In this paper, we present TREET, an experimentation and evaluation testbed based directly on that used in our investigations into security vulnerabilities in trust and reputation systems for marketplaces. We demonstrate the advantages of TREET, towards the development of more thorough, objective evaluations of trust and reputation systems.     

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.     

A simulation testbed for analyzing trust and reputation mechanisms in unreliable online markets

Modern online markets are becoming extremely dynamic, indirectly dictating the need for (semi-) autonomous approaches for constant monitoring and immediate action in order to satisfy one’s needs/preferences. In such open and versatile environments, software agents may be considered as a suitable metaphor for dealing with the increasing complexity of the problem. Additionally, trust and reputation have been recognized as key issues in online markets and many researchers have, in different perspectives, surveyed the related notions, mechanisms and models. Within the context of this work we present an adaptable, multivariate agent testbed for the simulation of open online markets and the study of various factors affecting the quality of the service consumed. This testbed, which we call Euphemus, is highly parameterized and can be easily customized to suit a particular application domain. It allows for building various market scenarios and analyzing interesting properties of e-commerce environments from a trust perspective. The architecture of Euphemus is presented and a number of well-known trust and reputation models are built with Euphemus, in order to show how the testbed can be used to apply and adapt models. Extensive experimentation has been performed in order to show how models behave in unreliable online markets, results are discussed and interesting conclusions are drawn.     

Security threats scenarios in trust and reputation models for distributed systems

Trust and reputation management over distributed systems has been proposed in the last few years as a novel and accurate way of dealing with some security deficiencies which are inherent to those environments. Thus, many models and theories have been developed in order to effective and accurately manage trust and reputation in those communities. Nevertheless, very few of them take into consideration all the possible security threats that can compromise the system. In this paper, we present some of the most important and critical security threats that could be applied in a trust and reputation scheme. We will describe and analyze each of those threats and propose some recommendations to face them when developing a new trust and reputation mechanism. We will also study how some trust and reputation models solve them. This work expects to be a reference guide when designing secure trust and reputation models.     

普适计算环境中防护策略的信任决策机制研究

普适计算信任模型中,存在自私用户为最大化自身利益而故意策略性谎报推荐信息的问题.文中提出了一种基于VCG(Vickrey-Clarke-Groves)机制的防护策略信任机制,用以获得用户的真实推荐.该机制实现了交互结果观测前的快速支付.一种基于连续多数加权算法的加权VCG防护策略机制被用于调整推荐权重.该文还给出了一般形式的信任决策机制并研究其激励相容特性以便可以构造更多的信任机制,这些信任机制结合已有的信任模型可以实现真实推荐.模拟结果显示,提出的信任机制有效,能保证自私用户提供诚实推荐.     

Trust-based security for wireless ad hoc and sensor networks

Wireless sensors networks are susceptible to a large number of security threats, and because of the communication, computation and delay constraints of most applications that run on top of these networks, traditional security mechanisms cannot be used. Trust and reputation have been recently suggested as an effective security mechanism for open environments such as the Internet, and considerable research has been done on modeling and managing trust and reputation. Using the trust and reputation management scheme to secure wireless sensor networks (WSNs) requires paying close attention to the incurred bandwidth and delay overhead, which have thus far been overlooked by most research work. In this paper, we propose a novel agent-based trust and reputation management scheme (ATRM) for wireless sensor networks. The objective of the scheme is to manage trust and reputation locally with minimal overhead in terms of extra messages and time delay. Throughout the entirety of this paper, we describe our scheme and prove its correctness. We will also present our extensive performance evaluation results, which clearly show that trust and reputation can be computed in wireless sensor networks with minimal overhead.     

Towards a trust,reputation and recommendation meta model

New trust, reputation and recommendation (TRR) models are continuously proposed. However, the existing models lack shared bases and goals. For this reason, in this work we define an innovative meta model to facilitate the definition and standardization of a generic TRR model. Following the meta model, researchers in the field will be able to define standard models, compare them with other models and reuse parts of them. A standardization is also needed to determine which properties should be present in a TRR model.In accordance with the objectives we were seeking, following our meta model, we have defined a pre-standardized TRR model for e-commerce, identified the fundamental concepts and the main features that contribute to form trust and reputation in that domain, respected the dependence on the context/role of trust and reputation, aggregated only homogeneous trust information; listed and shown how to defend from the main malicious attacks.     

基于信誉和风险评估的动态信任模型研究

网格环境中的信任问题是网格计算发展过程中必须解决的一个重要问题。网格节点间的行为信任具有动态性和不确定性。文章综合考虑了信任度评估的动态性和风险性,提出一种新的基于信誉和风险评估的动态信任模型。该模型引入惩罚机制、风险机制和推荐节点信任度更新机制,以评估信任关系的复杂性。仿真结果表明,该模型在有效抑制恶意节点上较已有模型有一定的改进。     

Flow-based reputation with uncertainty: evidence-based subjective logic

The concept of reputation is widely used as a measure of trustworthiness based on ratings from members in a community. The adoption of reputation systems, however, relies on their ability to capture the actual trustworthiness of a target. Several reputation models for aggregating trust information have been proposed in the literature. The choice of model has an impact on the reliability of the aggregated trust information as well as on the procedure used to compute reputations. Two prominent models are flow-based reputation (e.g., EigenTrust, PageRank) and subjective logic-based reputation. Flow-based models provide an automated method to aggregate trust information, but they are not able to express the level of uncertainty in the information. In contrast, subjective logic extends probabilistic models with an explicit notion of uncertainty, but the calculation of reputation depends on the structure of the trust network and often requires information to be discarded. These are severe drawbacks. In this work, we observe that the ‘opinion discounting’ operation in subjective logic has a number of basic problems. We resolve these problems by providing a new discounting operator that describes the flow of evidence from one party to another. The adoption of our discounting rule results in a consistent subjective logic algebra that is entirely based on the handling of evidence. We show that the new algebra enables the construction of an automated reputation assessment procedure for arbitrary trust networks, where the calculation no longer depends on the structure of the network, and does not need to throw away any information. Thus, we obtain the best of both worlds: flow-based reputation and consistent handling of uncertainties.     

基于群组的C2C电子商务信任模型研究

国内的电子商务平台大多采用eBay信任模型,对买家反馈评分进行简单累加得到卖家信誉值,未区分买家反馈评分的合理性及其参考价值的重要程度。为此,提出一种基于群组的C2C电子商务信任模型,通过计算买家与卖家的熟悉度,确定买家的可信度,综合考虑反馈评分、交易次数、交易价格、交易时间、以往买家的可信度对信誉的影响,构建电子商务信任模型。模拟实验结果证明,相比Sporas模型,该模型能为买家提供更全面、更准确的卖家信誉信息。     

FUZZY TRUST AGGREGATION AND PERSONALIZED TRUST INFERENCE IN VIRTUAL SOCIAL NETWORKS

Virtual marketplaces on the Web provide people with great facilities to buy and sell goods similar to conventional markets. In traditional business, reputation is subjectively built for known persons and companies as the deals are made in the course of time. As it is important to do business with trustful individuals and companies, there is a need to survive the reputation concept in virtual markets. Auction sites generally employ reputation systems based on feedbacks that provide a global view to a cyber dealer. In contrast to global trust, people usually infer their personal trust about someone whose reputation is completely or partially unknown by asking their trusted friends. Personal reputation is what makes a person trusted for some people and untrusted for others. There should be a facility for users in a virtual market to specify how much they trust a friend and also a mechanism that infers the trust of a user to another user who is not directly a friend of her. There are two main issues that should be addressed in trust inference. First, the trust modeling and aggregation problem needs to be challenged. Second, algorithms should be introduced to find and select the best paths among the existing trust paths from a source to a sink. First, as trust to a person can be stated more naturally using linguistic expressions, this work suggests employing linguistic terms for trust specification. To this end, corresponding fuzzy sets are defined for trust linguistic terms and a fuzzy trust aggregation method is also proposed. Comparing the fuzzy aggregation method to the existing aggregation methods shows superiority of fuzzy approach especially at aggregating contradictory information. Second, this paper proposes an incremental trust inference algorithm. The results show improvement in preciseness of inference for the proposed inference algorithm over the existing and recently proposed algorithm named TidalTrust.     

Reputation-based decisions for logic-based cognitive agents

Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification of axioms as first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models.     

Multifactor hierarchical fuzzy trust evaluation on peer-to-peer networks

Decentralized Peer-to-Peer (P2P) networks offer not only opportunities but also threats. Due to the autonomy, self-interest and heterogeneousness of peers, the interaction outcomes are uncertain. One way to minimize the threats in such an open environment is exploiting the reputation method to evaluate the trustworthiness and predict the future behaviors of peers. While most of the existing reputation-based trust models focus on preventing network from the malicious peers, peers’ capabilities to fulfill the tasks are mostly ignored. In this paper, we present a novel trust model MHFTrust which quantifies and compares the trustworthiness of peers based on hierarchical fuzzy system. Six capability factors are identified to describe the peers’ trust on the capability, and one security factor, named “Malicious behavior” is used to evaluate the peers’ trust on security. Our trust model consisted of local-trust computation based on fuzzy techniques and global reputation aggregation, which integrates feedback from other peers to produce a global reputation for each peer. Credibility and weight of feedback are introduced to facilitate the computation of global reputation. It is shown in simulation that our trust model greatly improves the efficiency of P2P system, while the number of inauthentic files on the network is significantly decreased.