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.

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.     

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.     

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。     

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.     

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.     

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.     

Alarm timing, trust and driver expectation for forward collision warning systems

In order to improve road safety, automobile manufacturers are now developing Forward Collision Warning Systems (FCWS). However, there has been insufficient consideration of how drivers may respond to FCWS. This driving simulator study focused on alarm timing and its impact on driver response to alarm. The experimental investigation considered driver perception of alarm timings and its influence on trust at three driving speeds (40, 60 and 70 mile/h) and two time headways (1.7 and 2.2 s). The results showed that alarm effectiveness varied in response to driving conditions. Alarm promptness had a greater influence on ratings of trust than improvements in braking performance enabled by the alarm system. Moreover, alarms which were presented after braking actions had been initiated were viewed as late alarms. It is concluded that drivers typically expect alarms to be presented before they initiate braking actions and when this does not happen driver trust in the system is substantially decreased.     

Artificial Neural Network support to monitoring of the evolutionary driven security aware scheduling in computational distributed environments

Monitoring of the system performance in highly distributed computing environments is a wide research area. In cloud and grid computing, it is usually restricted to the utilization and reliability of the resources. However, in today’s Computational Grids (CGs) and Clouds (CCs), the end users may define the special personal requirements and preferences in the resource and service selection, service functionality and data access. Such requirements may refer to the special individual security conditions for the protection of the data and application codes. Therefore, solving the scheduling problems in modern distributed environments remains still challenging for most of the well known schedulers, and the general functionality of the monitoring systems must be improved to make them efficient as schedulers supporting modules.In this paper, we define a novel model of security-driven grid schedulers supported by an Artificial Neural Network (ANN). ANN module monitors the schedule executions and learns about secure task–machine mappings from the observed machine failures. Then, the metaheuristic grid schedulers (in our case—genetic-based schedulers) are supported by the ANN module through the integration of the sub-optimal schedules generated by the neural network, with the genetic populations of the schedules.The influence of the ANN support on the general schedulers’ performance is examined in the experiments conducted for four types of the grid networks (small, medium, large and very large grids), two security scheduling modes—risky and secure scenarios, and six genetic-based grid schedulers. The generated empirical results show the high effectiveness of such monitoring support in reducing the values of the major scheduling criteria (makespan and flowtime), the run times of the schedulers and the grid resource failures.     

Stabilization and decay estimate for distributed bilinear systems

In this paper, we consider the problem of feedback stabilization for the distributed bilinear system y′(t)=Ay(t)+u(t)By(t). Here A is the infinitesimal generator of a linear C⁰ semigroup of contractions on a Hilbert space H and B:H→H is a linear bounded operator. A sufficient condition for feedback stabilization is given and explicit decay estimate is established. Applications to vibrating systems are presented.     

On the features and challenges of security and privacy in distributed internet of things

In the Internet of Things, services can be provisioned using centralized architectures, where central entities acquire, process, and provide information. Alternatively, distributed architectures, where entities at the edge of the network exchange information and collaborate with each other in a dynamic way, can also be used. In order to understand the applicability and viability of this distributed approach, it is necessary to know its advantages and disadvantages – not only in terms of features but also in terms of security and privacy challenges. The purpose of this paper is to show that the distributed approach has various challenges that need to be solved, but also various interesting properties and strengths.     

A familiarity-based trust model for effective selection of sellers in multiagent e-commerce systems

In electronic marketplaces, trust is modeled, for instance, in order to allow buying agents to make effective selection of selling agents. Familiarity is often considered to be an important factor in determining the level of trust. In previous research, familiarity between two agents has been simply assumed to be the similarity between them. We propose an improved familiarity measurement based on the exploration of factors that affect a human’s feelings of familiarity. We also carry out experiments to show that the trust model with our improved familiarity measurement is more effective and more stable.