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.     

Partial identities as a foundation for trust and reputation

This paper explores the relationships between the hard security concepts of identity and privacy on the one hand, and the soft security concepts of trust and reputation on the other hand. We specifically focus on two vulnerabilities that current trust and reputation systems have: the change of identity and multiple identities problems. As a result, we provide a privacy preserving solution to these vulnerabilities which integrates the explored relationships among identity, privacy, trust and reputation. We also provide a prototype of our solution to these vulnerabilities and an application scenario.     

Gossip-based aggregation of trust in decentralized reputation systems

Decentralized Reputation Systems have recently emerged as a prominent method of establishing trust among self-interested agents in online environments. A key issue is the efficient aggregation of data in the system; several approaches have been proposed, but they are plagued by major shortcomings. We put forward a novel, decentralized data management scheme grounded in gossip-based algorithms. Rumor mongering is known to possess algorithmic advantages, and indeed, our framework inherits many of their salient features: scalability, robustness, a global perspective, and simplicity. We demonstrate that our scheme motivates agents to maintain a very high reputation, by showing that the higher an agent’s reputation is above the threshold set by its peers, the more transactions it would be able to complete within a certain time unit. We analyze the relation between the amount by which an agent’s average reputation exceeds the threshold and the time required to close a deal. This analysis is carried out both theoretically, and empirically through a simulation system called GossipTrustSim. Finally, we show that our approach is inherently impervious to certain kinds of attacks. A preliminary version of this article appeared in the proceedings of IJCAI 2007.     

Parameter learning of personalized trust models in broker-based distributed trust management

Distributed trust management addresses the challenges of eliciting, evaluating and propagating trust for service providers on the distributed network. By delegating trust management to brokers, individual users can share their feedbacks for services without the overhead of maintaining their own ratings. This research proposes a two-tier trust hierarchy, in which a user relies on her broker to provide reputation rating about any service provider, while brokers leverage their connected partners in aggregating the reputation of unfamiliar service providers. Each broker collects feedbacks from its users on past transactions. To accommodate individual differences, personalized trust is modeled with a Bayesian network. Training strategies such as the expectation maximization (EM) algorithm can be deployed to estimate both server reputation and user bias. This paper presents the design and implementation of a distributed trust simulator, which supports experiments under different configurations. In addition, we have conducted experiments to show the following. 1) Personal rating error converges to below 5% consistently within 10,000 transactions regardless of the training strategy or bias distribution. 2) The choice of trust model has a significant impact on the performance of reputation prediction. 3) The two-tier trust framework scales well to distributed environments. In summary, parameter learning of trust models in the broker-based framework enables both aggregation of feedbacks and personalized reputation prediction.

Towards pre-standardization of trust and reputation models for distributed and heterogeneous systems

Different trust and/or reputation models have arisen in the last few years. All of them have certain key processes in common such as scoring, ranking, rewarding, punishing or gathering behavioral information. However, there is not a standardization effort for these kinds of models. Such effort would be beneficial for distributed systems such as P2P, ad-hoc networks, multi-agent systems or Wireless Sensor Networks. In this paper we present a pre-standardization approach for trust and/or reputation models in distributed systems. A wide review of them has been carried out, extracting common properties and providing some pre-standardization recommendations. A global comparison has been done for the most relevant models against these conditions, and an interface proposal for trust and/or reputation models has been proposed.     

Reputation-based Trust Model in Grid Security System

The security problem is a hot topic in grid research due to the dynamics and uncertainty of grid system. There are three entities defined as users, applications and resources in grid environment. In such situation, users are vulnerable to risk because of potential incomplete or distorted information provided by malicious resources, and as grid system grows tremendously in size, the possibility of users to attack the network by providing aggressive or vicious applications will increase greatly. Trust management is an effective method to maintain the credibility of the system and keep honesty of entities. This paper presents a trust model, which is used to compute and compare the trustworthiness of entities in the same autonomous and different domains. This model provides different methods to deal with the problems of users and related resources belonging to the same or different domains. Furthermore, a simulation experiment is provided to evaluate the trust model, and the simulation result shows it is effective to resolve the security problems in grid environment.     

The complexity of fixed point models of trust in distributed networks

In this paper we consider the complexity of some problems arising in a fixed point model of trust in large-scale distributed systems, based on the notion of trust structures introduced by Carbone, Nielsen and Sassone; a set of trust levels with two distinct partial orderings. In the trust model, a global trust state exists as the least fixed point of a collection of local policy functions of nodes in the network.     

Trust and reputation management for opportunistic dissemination

Nodes in opportunistic networks need to cooperate to disseminate data. However, employing intermediate nodes for dissemination leads to several security issues. Here, we propose an opportunistic trust and reputation mechanism entitled SAROS, which detects and avoids malicious nodes, i.e. nodes which, upon receiving messages for other interested peers, modify their content in order to spread false information. This can negatively affect the network, by polluting it with spam messages, or dropping messages of interest to the nodes in the network. By detecting and avoiding malicious nodes, SAROS is able to increase the percentage of correct messages that reach their destinations.     

Honesty and trust revisited: the advantages of being neutral about other’s cognitive models

Open distributed systems pose a challenge to trust modelling due to the dynamic nature of these systems (e.g., electronic auctions) and the unreliability of self-interested agents. The majority of trust models implicitly assume a shared cognitive model for all the agents participating in a society, and thus they treat the discrepancy between information and experience as a source of distrust: if an agent states a given quality of service, and another agent experiences a different quality for that service, such discrepancy is typically assumed to indicate dishonesty, and thus trust is reduced. Herein, we propose a trust model, which does not assume a concrete cognitive model for other agents, but instead uses the discrepancy between the information about other agents and its own experience to better predict the behavior of the others. This neutrality about other agents’ cognitive models allows an agent to obtain utility from lyres or agents having a different model of the world. The experiments performed suggest that this model improves the performance of an agent in dynamic scenarios under certain conditions such as those found in market-like evolving environments.     

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.     

一种P2P信任管理安全性协议

通常是通过建立相应的信任管理机制来遏制P2P网络中的节点的自私与欺作行为,但对信任管理机制本身存在的安全性问题关注则很少,使信任管理系统无法有效应付针对信任信息管理机制的安全攻击。针对这一问题,提出了确保信任管理安全性的安全协议(简称为SPRI)。分析及仿真实验表明,SPRI可以有效地遏制针对信任管理中的身份伪造及信息篡改的攻击,并且效率高,普适性强。     

Analysis of a trust model for SLA negotiation and enforcement in cloud markets

Online Reputation Systems help mitigate the information asymmetry between clients and providers in Cloud Computing Markets. However, those systems raise two main drawbacks: the disagreement for assuming the cost of ownership of such services and their vulnerability to reputation attacks from dishonest parties that want to increase their reputation. This article faces both problems by describing a decentralised (peer-to-peer) trust model that does not require the intervention of a central entity to manage it. This model includes mechanisms to allow participants to avoid dishonest behaviour from other peers: each client statistically analyses the external reports about providers and updates the trustworthiness of the peers. The trustworthiness values will be used to negotiate prices in later transactions. The trust model is then incorporated in the Service-Level Agreement negotiation and enforcement processes, prioritising trusted clients over non-trusted clients to minimise the consequences of low Quality of Service in relation to the trust of the provider, and incentivise accurate trust reports from the clients. Finally, this article evaluates and discusses the validity of the trust model under different attacks from dishonest clients and providers.     

移动Agent的安全威胁及对策分析

安全问题是移动Agent技术应用发展过程中的一个重要主题,移动Agent系统面临着许多新的安全问题,而现有的基于信息城堡模型的安全安全适应移动Agent系统,如果没有适当的安全对策,Agent技术的应用将会严重受阻。本文分析了能移动Agent系统有关的安全威胁,并提出了一些相应对策,这些安全对策可以直接集成到一个Agent系统上。     

GenTrust: A genetic trust management model for peer-to-peer systems

In recent years, peer-to-peer systems have attracted significant interest by offering diverse and easily accessible sharing environments to users. However, this flexibility of P2P systems introduces security vulnerabilities. Peers often interact with unknown or unfamiliar peers and become vulnerable to a wide variety of attacks. Therefore, having a robust trust management model is critical for such open environments in order to exclude unreliable peers from the system. In this study, a new trust model for peer-to-peer networks called GenTrust is proposed. GenTrust has evolved by using genetic programming. In this model, a peer calculates the trustworthiness of another peer based on the features extracted from past interactions and the recommendations. Since the proposed model does not rely on any central authority or global trust values, it suits the decentralized nature of P2P networks. Moreover, the experimental results show that the model is very effective against various attackers, namely individual, collaborative, and pseudospoofing attackers. An analysis on features is also carried out in order to explore their effects on the results. This is the first study which investigates the use of genetic programming on trust management.     

Computational trust and reputation models for open multi-agent systems: a review

In open environments, agents depend on reputation and trust mechanisms to evaluate the behavior of potential partners. The scientific research in this field has considerably increased, and in fact, reputation and trust mechanisms have been already considered a key elements in the design of multi-agent systems. In this paper we provide a survey that, far from being exhaustive, intends to show the most representative models that currently exist in the literature. For this enterprise we consider several dimensions of analysis that appeared in three existing surveys, and provide new dimensions that can be complementary to the existing ones and that have not been treated directly. Moreover, besides showing the original classification that each one of the surveys provide, we also classify models that where not taken into account by the original surveys. The paper illustrates the proliferation in the past few years of models that follow a more cognitive approach, in which trust and reputation representation as mental attitudes is as important as the final values of trust and reputation. Furthermore, we provide an objective definition of trust, based on Castelfranchi’s idea that trust implies a decision to rely on someone.     

A survey of trust in computer science and the Semantic Web

Trust is an integral component in many kinds of human interaction, allowing people to act under uncertainty and with the risk of negative consequences. For example, exchanging money for a service, giving access to your property, and choosing between conflicting sources of information all may utilize some form of trust. In computer science, trust is a widely used term whose definition differs among researchers and application areas. Trust is an essential component of the vision for the Semantic Web, where both new problems and new applications of trust are being studied. This paper gives an overview of existing trust research in computer science and the Semantic Web.     

Certification-based trust models in mobile ad hoc networks: A survey and taxonomy

A mobile ad hoc network is a wireless communication network which does not rely on a pre-existing infrastructure or any centralized management. Securing the exchanges in such network is compulsory to guarantee a widespread development of services for this kind of networks. The deployment of any security policy requires the definition of a trust model that defines who trusts who and how. There is a host of research efforts in trust models framework to securing mobile ad hoc networks. The majority of well-known approaches is based on public-key certificates, and gave birth to miscellaneous trust models ranging from centralized models to web-of-trust and distributed certificate authorities. In this paper, we survey and classify the existing trust models that are based on public-key certificates proposed for mobile ad hoc networks, and then we discuss and compare them with respect to some relevant criteria. Also, we have developed analysis and comparison among trust models using stochastic Petri nets in order to measure the performance of each one with what relates to the certification service availability.     

On security measures in distributed computer systems

Problems of data security are becoming increasingly acute. This study of these basic problems has been carried out in cooperation between scientists at the Department of Electrical Engineering at Linköping Institute of Technology and FOA 3 (Försvarets Forskningsanstalt Planeringsbyrån — Research Institute of National Defense, Operations Research Center).The situation considered is an office with individual work stations having desktop computers. These communicate among themselves and share a host computer of higher capacity including special purpose input/output equipment.Among questions discussed are:

• * problems related to the protection of data bases

• * ease of selectively designating addressees and authorisation to access data and messages; key distribution and protection

• * general requirements for equipment needed in such a local net environment.

No attempt has been made to predict when such distributed office computer systems will become commonly available in full scale. It does, however, seem clear that they are at least technically feasible already.     

Sophia: A local trust system to secure key-based routing in non-deterministic DHTs

Today, many distributed applications are typically deployed at a large scale, including Grid, web search engines and content distribution networks, and it is expected for their scale to grow more in terms of number of machines, locations and administrative domains. This poses many scalability issues related to the scale of the environment they run in. To explicitly address these issues, many distributed systems and everyday services use peer-to-peer (P2P) overlays to allow other parts of the system to benefit from the fault-tolerance and scalability of P2P technology. In particular, Distributed Hash Tables (DHTs), which implement a simple put-and-get interface to a dictionary-like data structure, have been extensively used to overcome the current limitations associated with the centralized and hierarchical components of distributed systems, including data management, resource discovery, job scheduling etc.     

Reviews with revenue in reputation: Credibility management method for consumer-opinion platforms

Nowadays everyone can review everything. Online customer-opinion platforms often help potential buyers take a decision. Sometimes, however, the multitude of contradictory opinions may confuse customers. Submitting a review requires time and effort, yet it only benefits others. Therefore jokes and shill reviews represent quite a percentage of reviews, because an average reviewer has no motivation to submit reviews. Reviews with Revenue in Reputation (RRR) Method is designed to encourage reviewers by gamification. RRR allows customers to easily spot credible reviews and restricts the number of reviews an unreliable reviewer can submit.