Six degrees of separation in multi-agent systems

Lunze, Jan

doi:10.1109/cdc.2016.7799322

Cited by 7 publications

(1 citation statement)

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“…where l is the number of feedback entities, and ρ(F k ) is the weight factor of feedback trust according to the ''six degrees of separation'' [21] it is expressed as formula ( 12):…”

Section: ) Weight Calculationmentioning

confidence: 99%

Research on the Tradeoff Between Privacy and Trust in Cloud Computing

Sun¹

2019

IEEE Access

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Promoting cloud services must consider the privacy requirements between the user and the provider. Both privacy and trust are related to knowledge about an entity; however, there is an inherent conflict between trust and privacy. In this paper, we research the relationship between privacy and trust in the cloud computing. First, we construct a trust model based on multiple factors, such as direct trust, trust risk, reward-punishment, and feedback trust; the weight of trust factor is determined by class diversity and information entropy theory. Second, we propose a novel privacy metric model with multiple factors, such as privacy preference, credential attribute, interaction history, and privacy feedback, and the weight of privacy factor is based on the maximum dispersion. Third, we propose a tradeoff between privacy and trust; both user and the provider can choose privacy protection or trust establishment priority by personal preference and requirement. Fourth, we demonstrate and compare the tradeoff between privacy and trust, interaction success rate, trust evaluation accuracy, and privacy disclosure rate by different experiments; these simulation results show that the privacy of each partner can be effectively protected.

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“…where l is the number of feedback entities, and ρ(F k ) is the weight factor of feedback trust according to the ''six degrees of separation'' [21] it is expressed as formula ( 12):…”

Section: ) Weight Calculationmentioning

confidence: 99%