Towards Reliable and Trustworthy Cooperation in Grid: A Pre-evaluating Set Based Trust Model

• Published in: Computational Science and Its Applications - ICCSA 2006 pp. 224 - 235
• Main Authors: Qu, Xiangli, Zhong, Jingwei, Yang, Xuejun
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Exact sciences and technology; Software; Theoretical computing; Social psychology; Confidence; Bias; Grid; Social network; Resource sharing; Electronic trade; Reputation; Transaction processing; Modeling; Parity
• ISBN: 9783540340799; 3540340793; 354034070X; 9783540340706
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11751649_24

Without reliable trust relationship between cooperative parities, the paradigm of large scale resource sharing and cooperated problem-solving as envisioned by most will not come true in Grid. With wide application in Electronic Commerce and online communities, Reputation mechanism emerges as a promising solution for trust establishment in Grid, especially between unknown entities. With little consideration of Grid’s distinct characteristics such as the sparseness of ratings and the strangeness of entities, most reputation evaluation methods currently available are not feasible to Grid. In this paper, we propose a pre-evaluating set based bias-tuned method for reputation evaluation in Grid. The introduction of the pre-evaluating set is to track an entity’s rating criteria, overcome the sparseness and strangeness mentioned above. With this pre-evaluating set, we can reasonably filter out malicious ratings by examining inconsistency between ratings to this set and to real transactions, effectively tune a rater’s bias to cater to the current evaluator’s criteria by interpolation approach and scientifically weight a rater’s rating in aggregation by the introduction of Criteria Coherent Degree.