187,251 research outputs found
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
Computing word-of-mouth trust relationships in social networks from Semantic Web and Web 2.0 data sources
Social networks can serve as both a rich source of new information and as a filter to identify the information most relevant to our specific needs. In this paper we present a methodology and algorithms that, by exploiting existing Semantic Web and Web2.0 data sources, help individuals identify who in their social network knows what, and who is the most trustworthy source of information on that topic. Our approach improves upon previous work in a number of ways, such as incorporating topic-specific rather than global trust metrics. This is achieved by generating topic experience profiles for each network member, based on data from Revyu and del.icio.us, to indicate who knows what. Identification of the most trustworthy sources is enabled by a rich trust model of information and recommendation seeking in social networks. Reviews and ratings created on Revyu provide source data for algorithms that generate topic expertise and person to person affinity metrics. Combining these metrics, we are implementing a user-oriented application for searching and automated ranking of information sources within social networks
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
A Survey on Trust Metrics for Autonomous Robotic Systems
This paper surveys the area of Trust Metrics related to security for
autonomous robotic systems. As the robotics industry undergoes a transformation
from programmed, task oriented, systems to Artificial Intelligence-enabled
learning, these autonomous systems become vulnerable to several security risks,
making a security assessment of these systems of critical importance.
Therefore, our focus is on a holistic approach for assessing system trust which
requires incorporating system, hardware, software, cognitive robustness, and
supplier level trust metrics into a unified model of trust. We set out to
determine if there were already trust metrics that defined such a holistic
system approach. While there are extensive writings related to various aspects
of robotic systems such as, risk management, safety, security assurance and so
on, each source only covered subsets of an overall system and did not
consistently incorporate the relevant costs in their metrics. This paper
attempts to put this prior work into perspective, and to show how it might be
extended to develop useful system-level trust metrics for evaluating complex
robotic (and other) systems
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