Trust Model Based on Similarity Measure of Vectors in P2P Networks

Guo, Leitao; Yang, Shanlin; Wang, Jing; Zhou, Jinyang

doi:10.1007/11590354_103

Cited by 17 publications

(4 citation statements)

References 10 publications

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“…This method can prevent collusive fraud well. But references [5,[15][16][17] do not study how to prevent concussive fraud.…”

Section: Related Workmentioning

confidence: 98%

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A Preventing Fraud Trust Model in P2P Networks

Liu

et al. 2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

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The existing trust models are not able to punish the concussive fraud in direct trust value and recommended trust value effectively. In the early stages of P2P networks, if some kinds of frauds are frequent in the meantime, the ability of trust model to prevent all the frauds is particularly important. To approve the trust model, first, referring to the idea of congestion control in computer network, "Additive Increase / Multiplicative Decrease", we put forward an adaptive punishment parameter in direct trust. Second, learning from intelligent algorithm to jump out of local optimum by random factors, we introduce a credibility parameter of trust value. And last, we import a punishment parameter in recommended trust, which is based on recommendation credibility. Simulation and analysis show that the approved trust model increases the success rate by 30% and advances the convergence time about 150 periods in the early stages of P2P network trading system.

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“…This method can prevent collusive fraud well. But references [5,[15][16][17] do not study how to prevent concussive fraud.…”

Section: Related Workmentioning

confidence: 98%

“…This method is useful when there is only collusive fraud but not concussive fraud. References [5,[15][16][17] see recommendation similarity between two nodes as recommendation credibility and calculate recommendation similarity by two trust vectors' cosine value or root mean square value. This method can prevent collusive fraud well.…”

Section: Related Workmentioning

confidence: 99%

A Preventing Fraud Trust Model in P2P Networks

Liu

et al. 2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

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“…Through the concepts of transaction and community context factor, modeling relationships can be customized according to semantics of transactions and community. Guo et al [5] keep similarity measurements in a vector and propose a method to compute the vector via time effect. Liu et al [6] propose an approach to detect malicious feedbacks and measure service quality with integrality, authenticity, and credibility metrics.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Consistency-based trust management in P2P networks

Şahin¹,

Can²

2018

Turk J Elec Eng & Comp Sci

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Abstract:Detecting malicious peers is a challenging task in peer-to-peer networks due to their decentralized structure and lack of central authority. Trust models can help identify malicious peers by maintaining information about peer relations and interactions. Keeping information about trust relations helps to reduce risks when providing or using services. This paper introduces two consistency concepts in trust management. Feedback consistency is used to evaluate how consistent feedback is with respect to past feedbacks. On the other side, peer consistency measures consistency of a peer's past feedbacks. These metrics help to reduce malicious interactions and increase successful downloads. Furthermore, the model offers better service quality for good peers by using consistency metrics. A file-sharing application is implemented on a simulation environment. The proposed model can effectively reduce the malicious download rate, even in 50% malicious environments, and increases successful download rates.

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