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.

Peer-to-peer (P2P) applications have seen an enormous success, and recently introduced P2P services have reached tens of millions of users. A feature that significantly contributes to the success of many P2P applications is user anonymity. However, anonymity opens the door to possible misuses and abuses, exploiting the P2P network as a way to spread tampered with resources, including Trojan Horses, viruses, and spam. To address this problem we propose a self-regulating system where the P2P network is used to implement a robust reputation mechanism. Reputation sharing is realized through a distributed polling algorithm by which resource requestors can assess the reliability of a resource offered by a participant before initiating the download. This way, spreading of malicious contents will be reduced and eventually blocked. Our approach can be straightforwardly piggybacked on existing P2P protocols and requires modest modifications to current implementations.

The use of previous direct interactions is probably the best way to calculate a reputation but, unfortunately this information is not always available. This is especially true in large multi-agent systems where interaction is scarce. In this paper we present a reputation system that takes advantage, among other things, of social relations between agents to overcome this problem.

For agents to function effectively in large and open networks, they must ensure that their correspondents, i.e., the agents they interact with, are trustworthy. Since no central authorities may exist, the only way agents can find trustworthy correspondents is by collaborating with others to identify those whose past behavior has been untrustworthy. In other words, finding trustworthy correspondents reduces to the problem of distributed reputation management. Our approach adapts the mathematical theory of evidence to represent and propagate the ratings that agents give to their correspondents. When evaluating the trustworthiness of a correspondent, an agent combines its local evidence (based on direct prior interactions with the correspondent) with the testimonies of other agents regarding the same correspondent. We experimentally studied this approach to establish that some important properties of trust are captured by it.

Reputation has been studied in many fields but few formal models have been defined up to now. In this paper we propose a new model for reputation that takes into account the social dimension of agents and a hierarchical ontology structure. This model is a natural extension of other models currently being used in the area of electronic commerce. We show how the model relates to these other systems and provide initial experimental results about the benefits of using a social view on the modeling of reputation.

Peer-to-Peer eCommerce communities are commonly perceived as an environment offering both opportunities and threats. One way to minimize threats in such an open community is to use community-based reputations, which can be computed, for example, through feedback about peers' transaction histories. Such reputation information can help estimating the trustworthiness and predicting the future behavior of peers. This paper presents a coherent adaptive trust model for quantifying and comparing the trustworthiness of peers based on a transaction-based feedback system. There are two main features of our model. First, we argue that the trust models based solely on feedback from other peers in the community is inaccurate and ineffective. We introduce three basic trust parameters in computing trustworthiness of peers. In addition to feedback a peer receives through its transactions with other peers, we incorporate the total number of transactions a peer performs, and the credibility of the feedback sources into the model for evaluating the trustworthiness of peers. Second, we introduce two adaptive factors, the transaction context factor and the community context factor, to allow the metric to adapt to different domains and situations and to address common problems encountered in a variety of online communities. We also developed a concrete method to validate the proposed trust model and obtained initial results, showing the feasibility and benefit of our approach.

We previously developed a social mechanism for distributed reputation management, in which an agent combines testimonies from several witnesses to determine its ratings of another agent. However, that approach does not fully protect against spurious ratings generated by malicious agents. This paper focuses on the problem of deception in testimony propagation and aggregation. We introduce some models of deception and study how to efficiently detect deceptive agents following those models. Our approach involves a novel application of the well-known weighted majority technique to belief function and their aggregation. We describe simulation experiments to study the number of apparently accurate witnesses found in different settings, the number of witnesses on prediction accuracy, and the evolution of trust networks.

Abstract. The scientific research in the area of computational mechanisms for trust and reputation in virtual societies is a recent discipline oriented to increase the reliability and performance of electronic communities. Computer science has moved from the paradigm of isolated machines to the paradigm of networks and distributed computing. Likewise, artificial intelligence is quickly moving from the paradigm of isolated and non-situated intelligence to the paradigm of situated, social and collective intelligence. The new paradigm of the so called intelligent or autonomous agents and Multi-Agent Systems (MAS) together with the spectacular emergence of the information society technologies (specially reflected by the popularization of electronic commerce) are responsible for the increasing interest on trust and reputation mechanisms applied to electronic societies. This review wants to offer a panoramic view on current computational trust and reputation models.

The proliferation of web-based social networks has lead to new innovations in social networking, particularly by allowing users to describe their relationships beyond a basic connection. In this dissertation, I look specifically at trust in web-based social networks, how it can be computed, and how it can be used in applications. I begin with a definition of trust and a description of several properties that affect how it is used in algorithms. This is complemented by a survey of web-based social networks to gain an understanding of their scope, the types of relationship information available, and the current state of trust. The computational problem of trust is to determine how much one person in the network should trust another person to whom they are not connected. I present two sets of algorithms for calculating these trust inferences: one for networks with binary trust ratings, and one for continuous ratings. For each rating scheme, the algorithms are built upon the defined notions of trust. Each is then analyzed theoretically and with respect to simulated and actual trust networks to determine how accurately they calculate the opinions of people in the system. I show that in both rating schemes the algorithms