Trust Model Architecture: Defining Prejudice by Learning
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.
KeywordsTrust Model Trust Evaluation Logical Rule Initial Trust Edward Elgar Publishing
Unable to display preview. Download preview PDF.
- 1.Hultkrantz, O., Lumsden, K.: E-commerce and consequences for the logistics industry. In: Proceedings for Seminar on The Impact of E-Commerce on Transport, Paris (2001)Google Scholar
- 3.Abdul-Rahman, A., Hailes, S.: A distributed trust model: new security paradigms workshop. In: Proceedings of the 1997 workshop on new security paradigms, Langdale, Cumbria, United Kingdom, pp. 48–60 (1998)Google Scholar
- 4.Ramchurn, S.R., Sierra, C., Jennings, N.R., Godo, L.: A Computational Trust Model for Multi-Agent Interactions based on Confidence and Reputation. In: Proceedings of 6th International Workshop of Deception, Fraud and Trust in Agent Societies, Melbourne, Australia, pp. 69–75 (2003)Google Scholar
- 5.Nooteboom, B.: Trust: forms, foundations, functions, failures, and figures. Edward Elgar Publishing, Ltd., Cheltenham UK. (2002) ISBN: 1 84064 545 8Google Scholar
- 7.Carbone, M., Nielsen, M., Sassone, V.: A formal model for trust in dynamic networks. In: Proceedings of the First International Conference on Software Engineering and Formal Methods, September 25-26, pp. 54–61 (2003)Google Scholar
- 8.Marx, M., Treur, J.: Trust dynamics formalised in temporal logic. In: Proceedings of the Third International Conference on Cognitive Science, ICCS, pp. 359–362 (2001)Google Scholar
- 10.Damiani, E., De Capitani di Vimercati, S., Samarati, P.: Managing Multiple and Dependable Identities. IEEE Internet Computing (November-December 2003)Google Scholar
- 11.Xiong, L., Lui, L.: A Reputation-Based Trust Model for Peer-to-Peer eCommerce Communities. In: IEEE International Conference on E-Commerce, June 24-27, pp. 275–284 (2003)Google Scholar
- 13.Wojcik, M., Venter, H.S., Eloff, J.H.P., Olivier, M.S.: Incorporating prejudice into trust models to reduce network overload. In: Proceedings of South African Telecommunications and Networking Application Conference (SATNAC 2005), SATNAC, Telkom. CD ROM Publication (2005)Google Scholar
- 14.Bagley, C., Verma, G., Mallick, K., Young, L.: Personality, self-esteem and prejudice. Saxon House. Teakfield Ltd., Westmead. Farnborough, Hants, England (1979) ISBN: 0 566 00265 5Google Scholar
- 16.Dasgupta, D.: Artificial neural networks and artificial immune systems: similarities and differences. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 1997), Orlando, October 12-15 (1997)Google Scholar