301 research outputs found

    Sybil attacks against mobile users: friends and foes to the rescue

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    Collaborative applications for co-located mobile users can be severely disrupted by a sybil attack to the point of being unusable. Existing decentralized defences have largely been designed for peer-to-peer networks but not for mobile networks. That is why we propose a new decentralized defence for portable devices and call it MobID. The idea is that a device manages two small networks in which it stores information about the devices it meets: its network of friends contains honest devices, and its network of foes contains suspicious devices. By reasoning on these two networks, the device is then able to determine whether an unknown individual is carrying out a sybil attack or not. We evaluate the extent to which MobID reduces the number of interactions with sybil attackers and consequently enables collaborative applications.We do so using real mobility and social network data. We also assess computational and communication costs of MobID on mobile phones

    Opportunistic mobile social networks: architecture, privacy, security issues and future directions

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    Mobile Social Networks and its related applications have made a very great impact in the society. Many new technologies related to mobile social networking are booming rapidly now-a-days and yet to boom. One such upcoming technology is Opportunistic Mobile Social Networking. This technology allows mobile users to communicate and exchange data with each other without the use of Internet. This paper is about Opportunistic Mobile Social Networks, its architecture, issues and some future research directions. The architecture and issues of Opportunistic Mobile Social Networks are compared with that of traditional Mobile Social Networks. The main contribution of this paper is regarding privacy and security issues in Opportunistic Mobile Social Networks. Finally, some future research directions in Opportunistic Mobile Social Networks have been elaborated regarding the data's privacy and security
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