DOI QR코드

DOI QR Code

Local Scalar Trust Metrics with a Fuzzy Adjustment Method

  • Seo, Yang-Jin (School of Computer Science and Engineering, ChungAng University) ;
  • Han, Sang-Yong (School of Computer Science and Engineering, ChungAng University)
  • Received : 2009.12.20
  • Accepted : 2010.03.07
  • Published : 2010.04.29

Abstract

The interactions between people who do not know each other have been greatly increased with the on-going increase of people's cyberspace activities. In this situation, there exist potential risk factors such as the possibility of fraud, so we need a method to reduce or eliminate those risk factors. Concerning this necessity, rating systems are widely used, and many trust metrics calculated from rate values that people give to each other are proposed to help them make decisions. However, the trust metrics decrease the accuracy, and this is caused by the different rating scales and ranges of each person. So, we propose a fuzzy adjustment method to solve this problem. It is possible to catch the exact meaning of the trust value that each person selects through applying fuzzy sets, which improve the accuracy of the trust metric calculated from the trust values. We have applied our fuzzy adjustment method to the TidalTrust algorithm, a representative algorithm for calculating the local scalar trust metric, and we performed an experimental evaluation with four data sets and three evaluation methods.

Keywords

References

  1. A. Josang, R. Ismail, C. Boyd, "A Survey of Trust and Reputation Systems for Online Service Provision," Decision Support System, pp.618-644, 2007.
  2. P. Resnick and R. Zeckhauser, "Trust among Strangers in Internet Transactions: Empirical Analysis of eBay's Reputation System", Technical report: University of Michigan, 2001.
  3. S. Grabner-Kraeuter, "The Role of Consumers' Trust in Online-Shopping," Journal of Business Ethics, vo.39, no.1-2, pp.43-50, 2002. https://doi.org/10.1023/A:1016323815802
  4. S. Grabner-Kräuter and E. A. Kaluscha, "Empirical research in on-line trust: a Review and Critical Assessment," International Journal of Human-Computer Studies, vol.58, no.6, pp. 783-812, 2003. https://doi.org/10.1016/S1071-5819(03)00043-0
  5. P. A. Pavlou, "Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model," International Journal of Electronic Commerce, vol.7, no.3, pp.101-134, 2003.
  6. K. K. Bharadwaj and M. Y. H. Al-Shamri, "Fuzzy Computational Models for Trust and Reputation Systems," Electronic Commerce Research and Applications, vol.8, no.1, pp.37-47, 2009. https://doi.org/10.1016/j.elerap.2008.08.001
  7. L. Xiong and L. Liu, "PeerTrust: Supporting Reputation-based Trust for Peer-to-peer Electronic Communities," IEEE Transactions on Knowledge and Data Engineering, vol.16, no.7, pp. 843-857, 2004. https://doi.org/10.1109/TKDE.2004.1318566
  8. S. D. Kamvar, M. T. Schlosser and H. Garcia-Molina, "The Eigentrust Algorithm for Reputation Management in P2P Networks," in Proc. of 12th Int Conf. on World Wide Web, pp. 640-651, 2003.
  9. Z. Liang and W. Shi, "PET: A PErsonalized Trust Model with Reputation and Risk Evaluation for P2P Resource Sharing," in Proc of. 38th Int. Annual Hawaii Conf. on System Sciences (HICSS'05), vol.7, pp.201b, 2005.
  10. P. Avesani, P. Mass, and R. Tiella, "A Trust-enhanced Recommender System Application: Moleskiing," in Proc of. 2005 ACM symposium on Applied computing, pp.1589-1593, 2005.
  11. F. E. Walter, S. Battiston, and F. Schweitzer, "A Model of a Trust-based Recommendation System on a Social Network," Autonomous Agents and Multi-Agent Systems, vol.16, no.1, pp. 57-74, 2008. https://doi.org/10.1007/s10458-007-9021-x
  12. C. Ziegler and G. Lausen, "Propagation Models for Trust and Distrust in Social Networks," Information Systems Frontiers, vol.7, no.4-5, pp.337-358, 2005. https://doi.org/10.1007/s10796-005-4807-3
  13. L. Page, S. Brin, R. Motwani, T. Winograd, "The PageRank Citation Ranking: Bringing Order to the Web," Technical Report, Stanford Digital Library Technologies Project, 1998.
  14. J. A. Golbeck, "Computing and Applying Trust in Web-based Social Networks," Ph.D. Dissertation University of Maryland-College Park, 2005.
  15. L. A. Zadeh, "Fuzzy Sets, Fuzzy Logic, Fuzzy Systems," World Scientific Press, 1996.
  16. R. Falcone, G. Pezzulo, and C. Castelfranchi, "A Fuzzy Approach to a Belief-Based Trust Computation," Lecture Notes in Computer Science, vol.2631, pp.55-60, 2003.
  17. S. Song, K. Hwang, and M. Macwa, "Fuzzy Trust Integration for Security Enforcement in Grid Computing," Lecture Notes in Computer Science, vol.3222, pp.9-21, 2004.
  18. S. Song, K. Hwang, R. Zhou, and Y. Kwok, "Trusted P2P Transactions with Fuzzy Reputation Aggregation," IEEE Internet Computing, vol.9, no.6, pp. 24-34, 2005. https://doi.org/10.1109/MIC.2005.136
  19. L. Mui, M. Mohtashemi, and A. Halberstadt, "A Computational Model of Trust and Reputation," in Proc of. 35th Int Hawaii Conf. on System Sciences, pp.188-196, 2002.
  20. J. A. Golbeck, "Computing with Social Trust (Human-Computer Interaction Series)," Springer, 2009.
  21. B. Klimt and Y. Yang, "Introducing the Enron Corpus," in Proc of. 1st Conf. on Email and Anti-Spam (CEAS), 2004.
  22. Enron Email Dataset, http://www.cs.cmu.edu/~enron/.