Modeling trust and reputation systems in hostile environments

Ghasempouri, Seyed Asgary; Ladani, Behrouz Tork

doi:10.1016/j.future.2019.05.017

Cited by 20 publications

(6 citation statements)

References 39 publications

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“…The increasing competition in the market (especially in countries offering low wages) motivate manufacturing enterprises to investigate and adopt new models of manufacturing and business and shift their long term supply chains to possibly short term broader manufacturing networks (Ghasempouri and Ladani 2019). The trend is towards industries in which the capabilities are accessible and employable from anywhere in the world at any time (Barenji 2013).…”

Section: Cloud Manufacturingmentioning

confidence: 99%

A blockchain technology based trust system for cloud manufacturing

Barenji

2021

J Intell Manuf

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Cloud manufacturing (CM) is a new networked manufacturing model that delivers various on-demand manufacturing capabilities to the consumers from the providers. In this model, the provider and consumer never meet each other, thus "trust" is the major enabler for starting a collaboration. In another word, a user must be sure that the requested capability will not be provided with malicious results, and the provider should ensure that the payment will be made on time. In this paper, a novel Blockchain Technology (BCT)-based trust system called "Blocktrust" is proposed to address the trust problem of the CM. First, the CM framework that contains the digital firm, capability pool, and digital certificate issuing units is developed, and then, the private blocktrust peer-to-peer network is proposed and implemented based on Hyperledger fabric. Finlay, the feasibility of the blocktrust is examined under different testing scenarios. The reason for using a private network instead of the public is placing restrictions on who is allowed to participate in the network and also enjoying a network with fast transaction speed. Experiments show that the proposed blocktrust embedded CM is credible and practical.

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Section: Cloud Manufacturingmentioning

confidence: 99%

A blockchain technology based trust system for cloud manufacturing

Barenji

2021

J Intell Manuf

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“…This order is the well-known ordering of partial functions and it turns the set of feedback traces into a partially ordered set 5 . A feedback trace α is maximal if for every feedback trace β, α ≤ β implies α = β.…”

Section: Trust Score Evaluationmentioning

confidence: 99%

“…For example, Shala et al [64] for machine to machine application services. Test agent evaluate the trust level (1)(2)(3)(4)(5) of services and, after computing the trust level, store this information on a public blockchain network. The aim of Kim et al [65] is to enhance trust be- [67].…”

Section: Related Workmentioning

confidence: 99%

“…Calculations of the trust score may be based on a user's trust, device's trust or a system's trust. It may depend upon the context and the requirements of the system [5]. In general, the trust score qualifies or quantifies the relationship between the trustor and the trustee within a specific context.…”

Section: Introductionmentioning

confidence: 99%

“…Note that α is not necessarily total so a more precise definition is that ε•α is a restriction of the identity function id. However, we simply use id to simplify the presentation 5. In fact, this order is directed-complete.…”

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confidence: 99%

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A Blockchain-Based Trust Management Framework with Verifiable Interactions

Pal¹,

Hill²,

Rabehaja³

et al. 2021

Preprint

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There has been tremendous interest in the development of formal trust models and metrics through the use of analytics (e.g., Belief Theory and Bayesian models), logics (e.g., Epistemic and Subjective Logic) and other mathematical models. The choice of trust metric will depend on context, circumstance and user requirements and there is no single best metric for use in all circumstances. Where different users require different trust metrics to be employed the trust score calculations should still be based on all available trust evidence. Trust is normally computed using past experiences but, in practice (especially in centralised systems), the validity and accuracy of these experiences are taken for granted. In this paper, we provide a formal framework and practical blockchain-based implementation that allows independent trust providers to implement different trust metrics in a distributed manner while still allowing all trust providers to base their calculations on a common set of trust evidence. Further, our design allows experiences to be provably linked to interactions without the need for a central authority. This leads to the notion of evidence-based trust with provable interactions. Leveraging blockchain allows the trust providers to offer their services in a competitive manner, charging fees

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