MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks

Hao, Fei; Min, Geyong; Lin, Man; Luo, Changqing; Yang, Laurence T.

doi:10.1109/tpds.2013.309

Cited by 66 publications

(23 citation statements)

References 25 publications

(26 reference statements)

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“…Social media emphasizes the active interaction from users, users can complete the social interactions by generating, consuming the contents over the social media. Recently, the wide usage of ubiquitous devices, such as mobile phones and smart devices, the mobile social media [15][16][17] attracts much attention from academia and industry.…”

Section: Application-oriented Social Computingmentioning

confidence: 99%

When social computing meets soft computing: opportunities and insights

Hao

Park

Pei

2018

Hum. Cent. Comput. Inf. Sci.

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The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision and noisy data from social networks. Thanks to the emerging soft computing techniques which unlike the conventional hard computing. It is widely used for coping with the tolerant of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) that is the process of investigating social structures and relevant properties through the use of network and graph theories. This paper aims to survey various SNA approaches using soft computing techniques such as fuzzy logic, formal concept analysis, rough sets theory and soft set theory. In addition, the relevant software packages about SNA are clearly summarized.

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Section: Application-oriented Social Computingmentioning

confidence: 99%

When social computing meets soft computing: opportunities and insights

Hao

Park

Pei

2018

Hum. Cent. Comput. Inf. Sci.

Self Cite

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“…Through the reverse cloud generator algorithm [23], we realize the conversion from quantitative trust vector to the three cloud parameters in the trust evaluation cloud TC(Ex, En, He).…”

Section: Trust Levelmentioning

confidence: 99%

A Cloud Theory-Based Trust Computing Model in Social Networks

Liu

Zhu

et al. 2016

Entropy

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How to develop a trust management model and then to efficiently control and manage nodes is an important issue in the scope of social network security. In this paper, a trust management model based on a cloud model is proposed. The cloud model uses a specific computation operator to achieve the transformation from qualitative concepts to quantitative computation. Additionally, this can also be used to effectively express the fuzziness, randomness and the relationship between them of the subjective trust. The node trust is divided into reputation trust and transaction trust. In addition, evaluation methods are designed, respectively. Firstly, the two-dimension trust cloud evaluation model is designed based on node's comprehensive and trading experience to determine the reputation trust. The expected value reflects the average trust status of nodes. Then, entropy and hyper-entropy are used to describe the uncertainty of trust. Secondly, the calculation methods of the proposed direct transaction trust and the recommendation transaction trust involve comprehensively computation of the transaction trust of each node. Then, the choosing strategies were designed for node to trade based on trust cloud. Finally, the results of a simulation experiment in P2P network file sharing on an experimental platform directly reflect the objectivity, accuracy and robustness of the proposed model, and could also effectively identify the malicious or unreliable service nodes in the system. In addition, this can be used to promote the service reliability of the nodes with high credibility, by which the stability of the whole network is improved.

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“…In [10] the authors adopted a dynamic approach to manage the trust formation and aggregation in response to the changing conditions of the network. For inferring trust in mobile social networks, a fuzzy trust inference mechanism is presented in [11]. The trust is evaluated through the building of a mobile context, which is the intersection of features such as time, location, prestige and the familiarity between users.…”

Section: A Trust Managementmentioning

confidence: 99%

Energy consumption evaluation of SAODV with trust management scheme under gray-hole attacks

Lupia

Rango

2015

2015 Wireless Telecommunications Symposium (WTS)

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Mobile ad-hoc networks allow communication between mobile nodes without the existence of an infrastructure, so they are adaptable also in hostile environments. Using the wireless communication leads to a lack of security. Due to the characteristics of mobile nodes, they have batteries as power source, so their energy is constrained. A routing protocol for MANETs has to take into account this constraint, limiting the amount of used energy. Introducing security measures to protect the network against malicious nodes has an impact on the energy amount required to nodes. To improve security of the network, there are already proposals that use cryptography to secure routing packets. In this contribution, a trust management system is introduced into the well-known protocol SAODV, to protect the network against effective attacks not protected by that protocol. The proposal is evaluated using gray hole attack and comparing performances and energy consumption between the SAODV with and without Trust Management Scheme.

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MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks

Cited by 66 publications

References 25 publications

When social computing meets soft computing: opportunities and insights

When social computing meets soft computing: opportunities and insights

A Cloud Theory-Based Trust Computing Model in Social Networks

Energy consumption evaluation of SAODV with trust management scheme under gray-hole attacks

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