scholarly journals Building behavioral AI using trust and reputation model based on mask model.

2019 ◽  
Vol 156 ◽  
pp. 387-394
Author(s):  
N.D. Shchepin ◽  
A.S. Zagarskikh
Keyword(s):  
Model Based ◽  
Trust And Reputation ◽  
Reputation Model

scholarly journals Context-Aware Trust and Reputation Model for Fog-Based IoT

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 31622-31632 ◽  
Author(s):  
Yasir Hussain ◽  
Huang Zhiqiu ◽  
Muhammad Azeem Akbar ◽  
Ahmed Alsanad ◽  
Abeer Abdul-Aziz Alsanad ◽  
...  
Keyword(s):  
Context Aware ◽  
Trust And Reputation ◽  
Reputation Model

A Reputation Model Based on Testimonies

2008 ◽  
pp. 37-52
Author(s):  
José de S.P. Guedes ◽  
Viviane Torres da Silva ◽  
Carlos J. P. de Lucena
Keyword(s):  
Model Based ◽  
Reputation Model

scholarly journals QADE: A NOVEL TRUST AND REPUTATION MODEL FOR HANDLING FALSE TRUST VALUES IN E–COMMERCE ENVIRONMENTS WITH SUBJECTIVITY CONSIDERATION

2015 ◽  
Vol 23 (1) ◽  
pp. 81-110 ◽  
Author(s):  
Eva ZUPANCIC ◽  
Denis TRCEK
Keyword(s):  
Trust Model ◽  
The Other ◽  
Reputation Management ◽  
Management Systems ◽  
Targeted Agents ◽  
Pairwise Similarity ◽  
Trust And Reputation ◽  
Brick And Mortar ◽  
Trading Partners ◽  
Reputation Model

Trust is essential to economic efficiency. Trading partners choose each other and make decisions based on how much they trust one another. The way to assess trust in e-commerce is different from those in brick and mortar businesses, as there are limited indicators available in online environments. One way is to deploy trust and reputation management systems that are based on collecting feedbacks about partners’ transactions. One of the problems within such systems is the presence of unfair ratings. In this paper, an innovative QADE trust model is presented, which assumes the existence of unfairly reported trust assessments. Subjective nature of trust is considered, where differently reported trust values do not necessarily mean false trust values but can also imply differences in dispositions to trust. The method to identify and filter out the presumably false values is defined. In our method, a trust evaluator finds other agents in society that are similar to him, taking into account pairwise similarity of trust values and similarity of agents’ general mindsets. In order to reduce the effect of unfair ratings, the values reported by the non-similar agents are excluded from the trust computation. Simulations have been used to compare the effectiveness of algorithms to decrease the effect of unfair ratings. The simulations have been carried out in environments with varying number of attackers and targeted agents, as well as with different kinds of attackers. The results showed significant improvements of our proposed method. On average 6% to 13% more unfair trust ratings have been detected by our method. Unfair rating effects on trust assessment were reduced with average improvements from 26% to 57% compared to the other most effective filtering methods by Whitby and Teacy.

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