ICIS '00 Proceedings of the twenty first international conference on Information systems
Belief Revision Process Based on Trust: Agents Evaluating Reputation of Information Sources
Proceedings of the workshop on Deception, Fraud, and Trust in Agent Societies held during the Autonomous Agents Conference: Trust in Cyber-societies, Integrating the Human and Artificial Perspectives
Trust and Reputation Model in Peer-to-Peer Networks
P2P '03 Proceedings of the 3rd International Conference on Peer-to-Peer Computing
Reputation-based framework for high integrity sensor networks
Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks
Using Trust for Secure Collaboration in Uncertain Environments
IEEE Pervasive Computing
A Probabilistic Framework for Decentralized Management of Trust and Quality
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
Towards pre-standardization of trust and reputation models for distributed and heterogeneous systems
Computer Standards & Interfaces
A medical data reliability assessment model
Journal of Theoretical and Applied Electronic Commerce Research
Journal of Theoretical and Applied Electronic Commerce Research
Behavior-based reputation management in P2P file-sharing networks
Journal of Computer and System Sciences
Bio-inspired enhancement of reputation systems for intelligent environments
Information Sciences: an International Journal
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Trust is an essential component for secure collaboration in uncertain environments. Trust management can be used to reason about future interactions between entities. In reputation-based trust management, an entity’s reputation is usually built on ratings from those who have had direct interactions with the entity. In this paper, we propose a Bayesian network based trust management model. In order to infer trust in different aspects of an entity’s behavior, we use multi-dimensional application specific trust values and each dimension is evaluated using a single Bayesian network. This makes it easy both to extend the model to involve more dimensions of trust and to combine Bayesian networks to form an opinion about the overall trustworthiness of an entity. Each entity can evaluate his peers according to his own criteria. The dynamic characteristics of criteria and of peer behavior can be captured by updating Bayesian networks. Risk is explicitly combined with trust to help users making decisions. In this paper, we show that our system can make accurate trust inferences and is robust against unfair raters.