scholarly journals Privacy analysis in mobile social networks: the influential factors for disclosure of personal data

2012 ◽  
Vol 5 (4) ◽  
pp. 315 ◽  
Author(s):  
Antonio Sapuppo
Kybernetes ◽  
2019 ◽  
Vol 48 (5) ◽  
pp. 906-929 ◽  
Author(s):  
Mohamad Hoseini ◽  
Fatemeh Saghafi ◽  
Emad Aghayi

Purpose A great number of people use mobile social networks (MSNs) to communicate, entertain, learn, search and get advice. Growth and survival of any community depends on the activities of its members in sharing information and knowledge. The purpose of this study is to assess the influential factors on knowledge sharing behavior in MSNs in different perspectives in a comprehensive manner. Design/methodology/approach A model of factors affecting knowledge sharing behavior in MSNs is proposed by applying the structural equation modeling and path analysis to data collected from a sample of users of a well-known MSN through a questionnaire. Findings This study supports the contributive aspects of trust and enjoying participation in sharing knowledge, while there is no significant correlation between perceived ease of use and knowledge sharing behavior in MSNs. Furthermore, intention to share knowledge can lead to actual behavior in MSNs environments. Practical implications The results obtained here provide a grasp of factors that influence knowledge sharing in mobile communities which would promote enhanced contribution towards their online communities by MSNs administrators. Originality/value A four-dimensional comprehensive model consisting of social, psychological, cultural and technological perspectives in one package is proposed here for knowledge sharing behavior in MSNs. Such a comprehensive perspective is overlooked in the existing literature.


2014 ◽  
Vol 36 (3) ◽  
pp. 613-625 ◽  
Author(s):  
Hai-Yang HU ◽  
Zhong-Jin LI ◽  
Hua HU ◽  
Ge-Hua ZHAO

Author(s):  
Seyyed Mohammad Safi ◽  
Ali Movaghar ◽  
Komeil Safikhani Mahmoodzadeh

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3994
Author(s):  
Yuxi Li ◽  
Fucai Zhou ◽  
Yue Ge ◽  
Zifeng Xu

Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k-nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k-nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others’ search right without updating their friends’ keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive L-semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user–server communication cost. Our results show that a decentralized and end-to-end encrypted k-nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.


2020 ◽  
Vol 5 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Gabriela Suntaxi ◽  
Aboubakr Achraf El Ghazi ◽  
Klemens Böhm

2012 ◽  
Vol 61 (7) ◽  
pp. 3209-3222 ◽  
Author(s):  
Xiaohui Liang ◽  
Xu Li ◽  
Tom H. Luan ◽  
Rongxing Lu ◽  
Xiaodong Lin ◽  
...  

2018 ◽  
Vol 15 (1) ◽  
pp. 319-329 ◽  
Author(s):  
Eric Ke Wang ◽  
Yueping Li ◽  
Yunming Ye ◽  
S. M. Yiu ◽  
Lucas C. K. Hui

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