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.
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Wojcik, M., Eloff, J.H.P., Venter, H.S. (2006). Trust Model Architecture: Defining Prejudice by Learning. In: Fischer-Hübner, S., Furnell, S., Lambrinoudakis, C. (eds) Trust and Privacy in Digital Business. TrustBus 2006. Lecture Notes in Computer Science, vol 4083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11824633_19
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DOI: https://doi.org/10.1007/11824633_19
Publisher Name: Springer, Berlin, Heidelberg
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