On the Simulation of Global Reputation Systems

Reputation systems evolve as a mechanism to build trust in virtual communities. In this paper we evaluate different metrics for computing reputation in multi-agent systems. We present a formal model for describing metrics in reputation systems and show how different well-known global reputation metrics are expressed by it. Based on the model a generic simulation framework for reputation metrics was implemented. We used our simulation framework to compare different global reputation systems to find their strengths and weaknesses. The strength of a metric is measured by its resistance against different threat-models, i.e. different types of hostile agents. Based on our results we propose a new metric for reputation systems.Reputation System, Trust, Formalization, Simulation

A Graph-Based Approach to Address Trust and Reputation in Ubiquitous Networks

The increasing popularity of virtual computing environments such as Cloud and Grid computing is helping to drive the realization of ubiquitous and pervasive computing. However, as computing becomes more entrenched in everyday life, the concepts of trust and risk become increasingly important. In this paper, we propose a new graph-based theoretical approach to address trust and reputation in complex ubiquitous networks. We formulate trust as a function of quality of a task and time required to authenticate agent-to-agent relationship based on the Zero-Common Knowledge (ZCK) authentication scheme. This initial representation applies a graph theory concept, accompanied by a mathematical formulation of trust metrics. The approach we propose increases awareness and trustworthiness to agents based on the values estimated for each requested task, we conclude by stating our plans for future work in this area

Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas

TruMet: An approach towards computing trust in multi-agent environment.

The growing popularity of multi-agent based approaches towards the formation and operation of virtual organizations (VO) present over the Internet, offer both opportunities and risks. One of the risks involved in such community is in the identification of trustworthy agent partners for transaction. In this paper we aim to describe our trust model which would contribute in measuring trust in the interacting agents. Named as TruMet, the trust metric model works on the basis of the parameters that we have identified as relevant to the features of the community. The model primarily analyses trust value on the basis of the agent¿s reputation, as provided by the agent itself, and the agent¿s aggregate rating as provided by the witness agents. The final computation of the trust value is given by a weighted average of these two components. While computing the aggregate rating, a weight based method has been adopted to discount the contribution of possibly un-fair ratings by the witness agents

Reputation-based trust evaluations through diversity

Non peer reviewedPostprin

Reputation Model with Forgiveness Factor for Semi-Competitive E-Business Agent Societies

In this paper we introduce a new reputation model for agents engaged in e-business transactions. Our model enhances classic reputation models by adding forgiveness factor and new sources of reputation information based on agents groups. The model was implemented using JADE multi-agent platform and initially evaluated for e-business scenarios comprising societies of buyer and seller agents.

Trust Model for Optimized Cloud Services

Cloud computing with its inherent advantages draws attention for business critical applications, but concurrently expects high level of trust in cloud service providers. Reputation-based trust is emerging as a good choice to model trust of cloud service providers based on available evidence. Many existing reputation based systems either ignore or give less importance to uncertainty linked with the evidence. In this paper, we propose an uncertainty model and define our approach to compute opinion for cloud service providers. Using subjective logic operators along with the computed opinion values, we propose mechanisms to calculate the reputation of cloud service providers. We evaluate and compare our proposed model with existing reputation models

Flow-based reputation: more than just ranking

The last years have seen a growing interest in collaborative systems like electronic marketplaces and P2P file sharing systems where people are intended to interact with other people. Those systems, however, are subject to security and operational risks because of their open and distributed nature. Reputation systems provide a mechanism to reduce such risks by building trust relationships among entities and identifying malicious entities. A popular reputation model is the so called flow-based model. Most existing reputation systems based on such a model provide only a ranking, without absolute reputation values; this makes it difficult to determine whether entities are actually trustworthy or untrustworthy. In addition, those systems ignore a significant part of the available information; as a consequence, reputation values may not be accurate. In this paper, we present a flow-based reputation metric that gives absolute values instead of merely a ranking. Our metric makes use of all the available information. We study, both analytically and numerically, the properties of the proposed metric and the effect of attacks on reputation values

A game theoretic model for digital identity and trust in online communities

Digital identity and trust management mechanisms play an important role on the Internet. They help users make decisions on trustworthiness of digital identities in online communities or ecommerce environments, which have significant security consequences. This work aims to contribute to construction of an analytical foundation for digital identity and trust by adopting a quantitative approach. A game theoretic model is developed to quantify community effects and other factors in trust decisions. The model captures factors such as peer pressure and personality traits. The existence and uniqueness of a Nash equilibrium solution is studied and shown for the trust game defined. In addition, synchronous and asynchronous update algorithms are shown to converge to the Nash equilibrium solution. A numerical analysis is provided for a number of scenarios that illustrate the interplay between user behavior and community effects