Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyse the current trends and developments in this area, and to propose a research agenda for trust and reputation systems.

Trust is an important aspect of decision making for Intemet applications and particularly influences the specification of security policy i.e. who is authorised to perform actions as well as the techniques needed to manage and implement security to and for the applications. This survey examines the various definitions of trust in the literature and provides a working definition of trust for Intemet applications. The properties of trust relationships are explained and classes of different types of trust identified in the literature are discussed with examples. Some influential examples of trust management systems are described.

When transacting and interacting through open computer networks, traditional methods used in the physical world for establishing trust can no longer be used. Creating virtual network substitutes with which people, organisations and software agents can derive trust in other parties requires computerised analysis of the underlying trust networks. This article describes an approach to trust network analysis using subjective logic (TNA-SL), that consists of the three following elements. Firstly it uses a concise notation with which trust transitivity and parallel combination of trust paths can be expressed. Secondly it defines a method for simplifying complex trust networks so that they can be expressed in this concise form. Finally it allows trust measures to be expressed as beliefs, so that derived trust can be automatically and securely computed with subjective logic. We compare our approach with trust derivation algorithms that are based on normalisation such as PageRank and EigenTrust. We also provide a numerical example to illustrates how TNA-SL can be applied.

Abstract — The performance of distributed networks depends on collaboration among distributed entities. To enhance security in distributed networks, such as ad hoc networks, it is important to evaluate the trustworthiness of participating entities since trust is the major driving force for collaboration. In this paper, we present a framework to quantitatively measure trust, model trust propagation, and defend trust evaluation systems against malicious attacks. In particular, we address the fundamental understanding of trust, quantitative trust metrics, mathematical properties of trust, dynamic properties of trust, and trust models. The attacks against trust evaluation are identified and defense techniques are developed. The proposed trust evaluation system is employed in ad hoc networks for securing ad hoc routing and assisting malicious node detection. The implementation is fully distributed. Simulations show that the proposed system can significantly improve network throughput as well as effectively detect malicious behaviors in ad hoc networks. Further, extensive simulations are performed to illustrate various attacks and the effectiveness of the proposed defense techniques. I.

Transacting and interacting through computer networks makes it difficult to use traditional methods for establishing trust between parties. Creating substitutes by which people, organisations and software agents can derive trust in others through computer networks requires computerised analysis of trust topologies. This paper describes diverse dimensions of trust that are needed for analysing trust topologies, and provides a notation with which to express trust relationships in terms of these dimensions. The result is a simple way of specifying topologies of trust from which derived trust relationships can be automatically and securely computed.

Abstract—The performance of ad hoc networks depends on cooperation and trust among distributed nodes. To enhance security in ad hoc networks, it is important to evaluate trustworthiness of other nodes without centralized authorities. In this paper, we present an information theoretic framework to quantitatively measure trust and model trust propagation in ad hoc networks. In the proposed framework, trust is a measure of uncertainty with its value represented by entropy. We develop four Axioms that address the basic understanding of trust and the rules for trust propagation. Based on these axioms, we present two trust models: entropy-based model and probability-based model, which satisfy all the axioms. Techniques of trust establishment and trust update are presented to obtain trust values from observation. The proposed trust evaluation method and trust models are employed in ad hoc networks for secure ad hoc routing and malicious node detection. A distributed scheme is designed to acquire, maintain, and update trust records associated with the behaviors of nodes ’ forwarding packets and the behaviors of making recommendations about other nodes. Simulations show that the proposed trust evaluation system can significantly improve the network throughput as well as effectively detect malicious behaviors in ad hoc networks. Index Terms—Ad hoc networks, security, trust modeling and evaluation. I.

To describe the concept of transitive trust in a simplified way, assume that agent A trusts agent B, and that agent B trusts agent C, then by transitivity, agent A trusts agent C. Trust transitivity manifests itself in various forms during real life human interaction, but can be challenging to concisely model in a formal way. In this paper we describe principles for expressing and analysing transitive trust networks, and define requirements for their validity. This framework can be used for modelling transitive trust in computerised interactions, and can be combined with algebras and algorithms for computing propagation of both trust and distrust. This is illustrated by an example where transitive trust is mathematically analysed with belief calculus.

Although much work in NLP has focused on simply determining what a document means, we also must know whether or not to believe it. Fact-finding algorithms attempt to identify the “truth ” among competing claims in a corpus, but fail to take advantage of the user’s prior knowledge and presume that truth itself is universal and objective rather than subjective. We introduce a framework for incorporating prior knowledge into any factfinding algorithm, expressing both general “common-sense ” reasoning and specific facts already known to the user as first-order logic and translating this into a tractable linear program. As our results show, this approach scales well to even large problems, both reducing error and allowing the system to determine truth respective to the user rather than the majority. Additionally, we introduce three new fact-finding algorithms capable of outperforming existing fact-finders in many of our experiments. 1

There are currently very few practical methods for assessing the quality of resources or the reliability of other entities in the online environment. This makes it difficult to make decisions about which resources can be relied upon and which entities it is safe to interact with. Trust and reputation systems are aimed at solving this problem by enabling service consumers to reliably assess the quality of services and the reliability of entities before they decide to use a particular service or to interact with or depend on a given entity. Such systems should also allow serious service providers and online players to correctly represent the reliability of themselves and the quality of their services. In the case of reputation systems, the basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive its reputation score. In the case of trust systems, the basic idea is to analyse and combine paths and networks of trust relationships in order to derive measures of trustworthiness of specific nodes. Reputation scores and trust measures can assist other parties in deciding whether or not to transact with a given party in the future, and whether it is safe to depend on a given resource or entity. This represents an incentive for good behaviour and for offering reliable resources, which thereby tends to have a positive effect on the quality of online markets and communities. This chapter describes the background, current status and future trend of online trust and reputation systems.

The general public is becoming increasingly dependent on distributed computer-based systems to perform basic, critical tasks. Few useful models and metrics are available to predict and measure users ’ trust levels in these systems. Current trust models typically focus on limited aspects of user trust – for example, authentication security issues – while ignoring other issues such as usability and reliability. This paper proposes an improved trust model and related metrics for distributed computer-based systems. The trust model will include variables for security, privacy, usability, reliability, and availability. Other factors (knowledge, experience, performance history, etc.) will be considered and may emerge as additional variables. The goal is to generate a model and metrics for trust that will be useful immediately, and will retain their value over many years, even as technology changes. Background Advances in network and microprocessor technology have increased the adoption of computer technology in areas such as consumer shopping, banking, voting, and automotive technology. At the same time, widespread proliferation of viruses and recent