BP-P2P: Belief propagation-based trust and reputation management for P2P networks

2012 ◽  
Author(s):  
Erman Ayday ◽  
Faramarz Fekri
Keyword(s):  
Belief Propagation ◽  
Reputation Management ◽  
P2p Networks ◽  
Trust And Reputation

scholarly journals Trust and Risk Assessment Model of Popular Software Based on Known Vulnerabilities

2017 ◽  
Vol 63 (3) ◽  
pp. 329-336 ◽  
Author(s):  
Marek Janiszewski ◽  
Anna Felkner ◽  
Jakub Olszak
Keyword(s):  
Risk Assessment ◽  
Assessment Model ◽  
Reputation Management ◽  
Management Systems ◽  
Risk Assessment Model ◽  
Software Management ◽  
P2p Networks ◽  
Trust And Reputation ◽  
Beneficial Impact ◽  
Available Information

Abstract This paper presents a new concept of an approach to risk assessment which can be done on the basis of publicly available information about vulnerabilities. The presented approach uses also the notion of trust and implements many concepts used in so called trust and reputation management systems (which are widely used in WSN, MANET or P2P networks, but also in ecommerce platforms). The article shows first outcomes obtained from the presented model. The outcomes demonstrate that the model can be implemented in real system to make software management more quantified and objective process, which can have real and beneficial impact on institutional security. In article, however the emphasis was set not on the model itself (which can be easily changed) but on the possibility of finding useful information about vulnerabilities.

scholarly journals Trust and Reputation Management for Securing Collaboration in 5G Access Networks: The Road Ahead

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 62542-62560
Author(s):  
Israr Ahmad ◽  
Kok-Lim Alvin Yau ◽  
Mee Hong Ling ◽  
Sye Loong Keoh
Keyword(s):  
Access Networks ◽  
Reputation Management ◽  
The Road ◽  
Trust And Reputation

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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