Trust model architecture: defining prejudice by learning

Authors:
M. Wojcik;J. H. P. Eloff;H. S. Venter
Affiliations:
Information and Computer Security Architectures Research Group (ICSA), Department of Computer Science, University of Pretoria;Information and Computer Security Architectures Research Group (ICSA), Department of Computer Science, University of Pretoria;Information and Computer Security Architectures Research Group (ICSA), Department of Computer Science, University of Pretoria
Venue:
TrustBus'06 Proceedings of the Third international conference on Trust, Privacy, and Security in Digital Business
Year:
2006

Citing 6
Cited 3

A distributed trust model

NSPW '97 Proceedings of the 1997 workshop on New security paradigms
Multiagent learning using a variable learning rate

Artificial Intelligence
Formal Analysis of Models for the Dynamics of Trust Based on Experiences

MAAMAW '99 Proceedings of the 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World: MultiAgent System Engineering
A reputation-based trust model for peer-to-peer ecommerce communities [Extended Abstract]

Proceedings of the 4th ACM conference on Electronic commerce
Technologies for Trust in Electronic Commerce

Electronic Commerce Research
Managing Multiple and Dependable Identities

IEEE Internet Computing

A methodology to quantify failure for risk-based decision support system in digital business ecosystems

Data & Knowledge Engineering
Determining the Failure Level for Risk Analysis in an e-Commerce Interaction

Advances in Web Semantics I
Facing openness with socio-cognitive trust and categories

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One

Quantified Score

Hi-index	0.00

Visualization

Abstract

Due to technological change, businesses have become information driven, wanting to use information in order to improve business function. This perspective change has flooded the economy with information and left businesses with the problem of finding information that is accurate, relevant and trustworthy. Further risk exists when a business is required to share information in order to gain new information. Trust models allow technology to assist by allowing agents to make trust decisions about other agents without direct human intervention. Information is only shared and trusted if the other agent is trusted. To prevent a trust model from having to analyse every interaction it comes across – thereby potentially flooding the network with communications and taking up processing power – prejudice filters filter out unwanted communications before such analysis is required. This paper, through literary study, explores how this is achieved and how various prejudice filters can be implemented in conjunction with one another.