A Distributed Trust Model for Peer-to-Peer Networks Based on Multi-Dimension-History Vector

Tan, Zhenhua; Wang, Xingwei; Cheng, Wei; Chang, Guiran; Zhu, Zhiliang

doi:10.3724/sp.j.1016.2010.01725

Cited by 6 publications

(18 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The result proves that our scheme"Infer-Trust" is more efficient to infer trust during the intensive distrust relations environment. In fact, in our works [14] and [18], the other two methods prove to be invalid when trust relations are sparse.…”

Section: Trust Inference Performance During Intensive Distrust Relmentioning

confidence: 81%

“…Figure 9 shows one of the result with marked node pair <i=12, j=253>. In our former works, we have simulated and proved that the other two methods in [14] and [18] are effective to infer trust during intensive trust environment. Now, as we can see from the above results, the "Infer-Trust" could normally infer a rational value, just as the other method.…”

Section: B Trust Inference Performance During Intensive Trust Relationsmentioning

confidence: 88%

“…We improve the simulation environment based on our former simulation platform [14] and [18]. In addition, we design three kinds of nodes for simulation.…”

Section: Simulations and Analysismentioning

confidence: 99%

“…However, its time factor is only linear and there is no clear method to resist team malicious behaviors. Tan and Cheng et al [14] presented a global trust model with correlation factor based on communication history, and improved the time factor with exponential equation. It shows a rational history vector and presents three trust models with multi-dimensional trust factors.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Distributed Trust Inference Model Based on Probability and Balance Theory for Peer-To-Peer systems

Tan

Yang

Cheng

2012

International Journal on Smart Sensing and Intelligent Systems

Self Cite

View full text Add to dashboard Cite

Abstract-Researchers have done much around how to measure trust degrees or levels by local and global style in a given distributed network. However, how to infer trust degree for a strange node efficiently in a large-scale distributed environment was little done. This paper focuses on this problem, and proposes a novel trust model based on balance theory and probability theory. We firstly design a simple direct trust model for evidence computing, then construct trust relations network and trust inference network based on direct trust network. In order to discover trusted evidence chains during complex relations, we design two inference rules and propose mathematics models to infer indirect trust value based on Markov chain theory. Simulations proved the rightness and effectiveness in intensive trust relations environment and intensive distrust environment.Index terms: trust model, trust inference, peer-to-peer security, distributed system, trusted evidence chain, trust probability, peer-to-peer network.

show abstract

Section: Trust Inference Performance During Intensive Distrust Relmentioning

confidence: 81%

Section: B Trust Inference Performance During Intensive Trust Relationsmentioning

confidence: 88%

“…We improve the simulation environment based on our former simulation platform [14] and [18]. In addition, we design three kinds of nodes for simulation.…”

Section: Simulations and Analysismentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Distributed Trust Inference Model Based on Probability and Balance Theory for Peer-To-Peer systems

Tan

Yang

Cheng

2012

International Journal on Smart Sensing and Intelligent Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, the reputation algorithm is lack of accuracy and robustness. In order to improve the accuracy of trust evaluation results, rating, probability, fuzzy logic, subjective logic, and D_S evidence theory were used to calculate of reliability . In these methods, the uncertainty of trust is described on mathematical theory.…”

Section: Introductionmentioning

confidence: 99%

A dynamic trust evaluation mechanism based on affective intensity computing

Zhang

Wang

Zhao

et al. 2016

Security Comm. Networks

View full text Add to dashboard Cite

A complete description of trust relationship is key to construct a high precision trust model. But most of existing models not only miss the negative information and the hesitation information of trust, but also ignore the discordance between text comments and ratings. To solve the problems, a dynamic trust evaluation model based on the affect intensity is proposed. In the model, the intensity of sentimental polarities are calculated from words in comments. The corresponding relation between evaluations of trust property and the emotional intensity vector can be also described. Time series weights to distinguish the importance of transaction at different time are determined by the inverse form of exponential distribution. So the dynamic attenuation of trust can be described. To improve the polymerization ability of trust information, the local trust, the feedback trust and the overall trust are calculated by operators of fuzzy logic. The experimental results show the proposed model can effectively describe the trust relationship between nodes to identify and eliminate various malicious attacks significantly. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Improve Predictive Accuracy by Identifying Collusions in P2P Recommender Systems

Chang

Tan

Yang

2018

Advances in Multimedia Information Processing – PCM 2018

View full text Add to dashboard Cite

A Distributed Trust Model for Peer-to-Peer Networks Based on Multi-Dimension-History Vector

Cited by 6 publications

References 4 publications

Distributed Trust Inference Model Based on Probability and Balance Theory for Peer-To-Peer systems

Distributed Trust Inference Model Based on Probability and Balance Theory for Peer-To-Peer systems

A dynamic trust evaluation mechanism based on affective intensity computing

Improve Predictive Accuracy by Identifying Collusions in P2P Recommender Systems

Contact Info

Product

Resources

About