Peer-to-peer (P2P) online communities are commonly perceived as an environment offering both opportunities and threats. One way to

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.

Many players in electronic markets have to cope with much higher amount of uncertainty as to quality and reliability of the products they buy and the information they obtain from other peers in the respective online business communities.

The Semantic Web enables intelligent agents to “outsource ” knowledge, extending and enhancing their limited knowledge bases. An open question is how agents can efficiently and effectively access the vast knowledge on the inherently open and dynamic Semantic Web. The problem is not that of finding a source for desired information, but deciding which among many possibly inconsistent sources is most reliable. We propose an approach to agent knowledge outsourcing inspired by the use trust in human society. Trust is a type of social knowledge and encodes evaluations about which agents can be taken as reliable sources of information or services. We focus on two important practical issues: learning trust and justifying trust. An agent can learn trust relationships by reasoning about its direct interactions with other agents and about public or private reputation information, i.e., the aggregate trust evaluations of other agents. We use the term trust justification to describe the process in which an agent integrates the beliefs of other agents, trust information, and its own beliefs to update its trust model. We describe the results of simulation experiments of the use and evolution of trust in multi-agent systems. Our experiments demonstrate that the use of explicit trust knowledge can significantly improve knowledge outsourcing performance. We also describe a collaborative trust justification technique that focuses on reducing search complexity, handling inconsistent knowledge, and avoiding error propagation. 1.

Abstract: 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. *

Collaboration within and among companies or in (virtual) communities faces a big challenge by the increased dynamics of today's economical and industrial world. New tools have the potential to overcome one drawback of traditional systems: the effort needed to establish stable infrastructures. Peer-to-Peer (P2P) software...

Online reputation systems are currently receiving increased attention while online inter-actions are flourishing. However, they lack one important feature: globality. Users are allowed to build a reputation within one online community, and sometimes several repu-tations within several independent online communities, but each reputation is only valid within the corresponding community. Moreover, such reputation is usually aggregated by the provider of the online reputation system, giving the querying agent no say in the process. This thesis presents a novel solution to this problem. We conduct a literature review on existing trust and reputation models and classify these models using proper criteria. We introduce an online reputation system that collects reputation information about a ratee from several online communities and allows for this information to be ag-gregated according to the inquiring agent’s own requirements. We propose a configurable aggregation method for local and global reputation based on a discrete statistical model, taking into account several factors and parameters that qualify the reputation. We also implement a prototype of the proposed reputation computation model.

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Today’s online reputation systems lack one important feature: globality. Users build a reputation within one community, and sometimes several reputations within several communities, but each reputation is only valid within the corresponding community. Moreover, such reputation is usually aggregated by the online platform’s provider, giving the inquiring agent no say in the process. This paper proposes one way of dealing with this problem. We introduce an online reputation centralizer that collects raw reputation data about users from several online communities and allows for it to be aggregated according to the inquiring agent’s requirements, using a stochastic trust model, and taking into account factors that qualify a user’s reputation. Categories and Subject Descriptors