Assessment of multi-hop interpersonal trust in social networks by Three-Valued Subjective Logic

Liu, Guangchi; Yang, Qing; Wang, Honggang; Lin, Xiaodong; Wittie, Mike P.

doi:10.1109/infocom.2014.6848107

Cited by 73 publications

(47 citation statements)

References 21 publications

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“…With positive, negative, and uncertain interactions, the closeness between vehicles can be modeled as an opinion, according to the three-valued subjective logic (3VSL) [14]. This model will be detained in the next section.…”

Section: Network Maintenancementioning

confidence: 99%

“…First, subjective logic does not work in complex networks. This problem is addressed in 3VSL [14] by distinguishing distorting opinions from original opinions. The second issue of subjective logic is that it has difficulty defining the discounting operator.…”

Section: B Three-valued Subjective Logicmentioning

confidence: 99%

“…The recursive algorithm proposed in [14] can be used to assess the closeness between two vehicles that have no direct interactions, i.e., no information exchanging between them.…”

Section: Combining Operationmentioning

confidence: 99%

“…Particularly, we propose to construct the initial social connections between vehicles based on the points of interest (POIs) they visited. We then introduce in Section III how to model the closeness between vehicles based on the three-valued subjective logic [14]. In Section IV, we discuss the security and privacy concerns and possible solutions in the proposed architecture.…”

Section: Introductionmentioning

confidence: 99%

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An Architecture of Cloud-Assisted Information Dissemination in Vehicular Networks

Yang

Zhu

2016

IEEE Access

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Vehicular network technology allows vehicles to exchange real-time information between each other, which plays a vital role in the development of future intelligent transportation systems (ITSs). Existing research on vehicular networks assumes that each vehicle broadcasts collected information to neighboring vehicles so that information is shared among vehicles. The fundamental problem of what information is delivered with which vehicle(s), however, has not been adequately studied. We propose an innovative cloud-assisted architecture to facilitate intelligent information dissemination among vehicles. Within the novel architecture, virtual social connections between vehicles are created and maintained on the cloud. Vehicles with similar driving histories are considered friends in a vehicular social network (VSN). The closeness of the relation between two vehicles in a VSN is then modeled by the three-valued subjective logic model. Based on the closeness between vehicles, only relevant information will be delivered to vehicles that are likely interested in it. The cloud-assisted architecture coordinates vehicular social connection construction, VSN maintenance, vehicle closeness assessment, and information dissemination.

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Section: Network Maintenancementioning

confidence: 99%

Section: B Three-valued Subjective Logicmentioning

confidence: 99%

“…The recursive algorithm proposed in [14] can be used to assess the closeness between two vehicles that have no direct interactions, i.e., no information exchanging between them.…”

Section: Combining Operationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Architecture of Cloud-Assisted Information Dissemination in Vehicular Networks

Yang

Zhu

2016

IEEE Access

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“…Due to the propagative nature of trust, focus on indirect trust inference mainly lies in modeling trust propagation along trust relations between people who do not have direct trust connections [8]. To model and determine the trust between two users having no direct interaction in an OSN, there are mainly two method types in the literature: topology-based and evidence-based.…”

Section: Indirect Trust Inferencementioning

confidence: 99%

Toward trustworthy vehicular social networks

Yang

Wang

2015

IEEE Commun. Mag.

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VFLF: A verifiable federated learning framework against malicious aggregators in Industrial Internet of Things

Zhou

Tian

Peng

et al. 2022

Concurrency and Computation

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As an important approach to overcome data silos and privacy concerns in deep learning, federated learning, which can jointly train the global model and keep data local, has shown remarkable performance in a range of industrial applications. However, federated learning still suffers from the problem that shared gradients may be subject to tampering, inference functions, and falsification. To address this issue, we propose a verifiable federated learning framework to deal with malicious aggregators. Initially, we propose a reputation calculation mechanism to solve the problem of selecting a reliable aggregator based on a multiweight subjective logic model. Furthermore, we design a verifiable federated learning scheme to ensure data confidentiality, integrity, and verifiability, as well as support the client's dynamic withdrawal. Security analyses indicate that our framework is secure against malicious adversaries. Furthermore, experimental results on real datasets show that our verifiable federated learning has high accuracy and feasible efficiency.

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Assessment of multi-hop interpersonal trust in social networks by Three-Valued Subjective Logic

Cited by 73 publications

References 21 publications

An Architecture of Cloud-Assisted Information Dissemination in Vehicular Networks

An Architecture of Cloud-Assisted Information Dissemination in Vehicular Networks

Toward trustworthy vehicular social networks

VFLF: A verifiable federated learning framework against malicious aggregators in Industrial Internet of Things

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