Quantifying trust relationships based on real-world social interactions

Palaghias, Niklas; Loumis, Nikos; Georgoulas, Stylianos; Moessner, Klaus

doi:10.1109/icc.2016.7510835

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…Palaghias et al [90] proposed an opportunistic sensing system called MobTrust to quantify and derive trust relationships among users through mobile phones by detecting real-world social interactions. In their system [90], trust assessment is based on various attributes that are taken out from users’ social interactions such as relative-orientation, frequency and duration of interactions. However, considering only these social attributes in the trust management model is not an effective solution because of the difference in assumptions on the association between the behaviors of the entities.…”

Section: Internet-of-things Trust Modelsmentioning

confidence: 99%

Internet-of-Things and Information Fusion: Trust Perspective Survey

Azzedin

Ghaleb

2019

Sensors

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The advent of Internet-of-Things (IoT) is creating an ecosystem of smart applications and services enabled by a multitude of sensors. The real value of these IoT smart applications comes from analyzing the information provided by these sensors. Information fusion improves information completeness/quality and, hence, enhances estimation about the state of things. Lack of trust and therefore, malicious activities renders the information fusion process and hence, IoT smart applications unreliable. Behavior-related issues associated with the data sources, such as trustworthiness, honesty, and accuracy, must be addressed before fully utilizing these smart applications. In this article, we argue that behavior trust modeling is indispensable to the success of information fusion and, hence, to smart applications. Unfortunately, the area is still in its infancy and needs further research to enhance information fusion. The aim of this article is to raise the awareness and the need of behavior trust modelling and its effect on information fusion. Moreover, this survey describes IoT architectures for modelling trust as well as classification of current IoT trust models. Finally, we discuss future directions towards trustworthy reliable fusion techniques.

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Section: Internet-of-things Trust Modelsmentioning

confidence: 99%

Internet-of-Things and Information Fusion: Trust Perspective Survey

Azzedin

Ghaleb

2019

Sensors

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“…Some researchers believe that acquiring trust from real-world social interactions can play an important role in understanding social behavior. Therefore, an opportunistic sensing system [15] is proposed, which can detect social interactions based on the real world and acquire and quantify trust relationships among people through smartphones.…”

Section: Related Workmentioning

confidence: 99%

Quantitative social relations based on trust routing algorithm in opportunistic social network

Chen

et al. 2019

J Wireless Com Network

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The trust model is widely used in the opportunistic social network to solve the problem of malicious nodes and information flooding. The previous method judges whether the node is a cooperative node through the identity authentication, forwarding capability, or common social attribute of the destination node. In real applications, this information does not have integrity and does not take into account the characteristics and dynamic adaptability of nodes, network structures, and the transitivity of social relationships between nodes. Therefore, it may not be effective in solving node non-cooperation problems and improving transmission success rate. To address this problem, the proposed node social features relationship evaluation algorithm (NSFRE) establishes a fuzzy similarity matrix based on various features of nodes. Each node continuously and iteratively deletes the filtered feature attributes to form a multidimensional similarity matrix according to the confidence level and determines the weights under different feature attributes. Then, the social relations of nodes are further quantified. The experimental results show that, compared with the traditional routing algorithm, NSFRE algorithm can effectively improve the transmission success rate, reduce transmission delay, ensure the safe and reliable transmission of information in the network, and require low buffer space and computing capacity.

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“…As a result, values they got from the computation mechanism do not reflect some key characteristics of trust, thus cannot quantify as trust. An interesting article is about judging trust based on several features extracted from social interactions such as spatiality, relative orientation, frequency of interactions, and duration of interactions [ 30 ]. However, this information is not sufficient to accurately derive trust due to a variety of assumptions on relations between trust and behaviors of entities which are sometimes not correct.…”

Section: Trust Evaluation Model: Background and Provisionsmentioning

confidence: 99%

Toward a Trust Evaluation Mechanism in the Social Internet of Things

Truong

Lee

Askwith

et al. 2017

Sensors

106

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In the blooming era of the Internet of Things (IoT), trust has been accepted as a vital factor for provisioning secure, reliable, seamless communications and services. However, a large number of challenges still remain unsolved due to the ambiguity of the concept of trust as well as the variety of divergent trust models in different contexts. In this research, we augment the trust concept, the trust definition and provide a general conceptual model in the context of the Social IoT (SIoT) environment by breaking down all attributes influencing trust. Then, we propose a trust evaluation model called REK, comprised of the triad of trust indicators (TIs) Reputation, Experience and Knowledge. The REK model covers multi-dimensional aspects of trust by incorporating heterogeneous information from direct observation (as Knowledge TI), personal experiences (as Experience TI) to global opinions (as Reputation TI). The associated evaluation models for the three TIs are also proposed and provisioned. We then come up with an aggregation mechanism for deriving trust values as the final outcome of the REK evaluation model. We believe this article offers better understandings on trust as well as provides several prospective approaches for the trust evaluation in the SIoT environment.

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Quantifying trust relationships based on real-world social interactions

Cited by 9 publications

References 28 publications

Internet-of-Things and Information Fusion: Trust Perspective Survey

Internet-of-Things and Information Fusion: Trust Perspective Survey

Quantitative social relations based on trust routing algorithm in opportunistic social network

Toward a Trust Evaluation Mechanism in the Social Internet of Things

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