A Scalable Approach for Achieve the Anonymity of the Neighboring Node in Mobile Social Networks
Keywords:
Mobile Opportunistic Social Networks, Anonymity, Encountering Information.Abstract
Social Anonymity is a serious threat to users of mobile social networks. Anonymizing genuine IDs among neighbor nodes explains such concerns. Be that as it may, this prevents nodes from gathering genuine ID-based encountering data, which is expected to help mobile opportunistic social networks services. Along these lines, in this paper, This work Propose that scalable fine grained approach that can support both anonymizing genuine IDs among neighbor nodes furthermore, collecting genuine ID-based encountering data. For node anonymity, two encountering nodes communicate anonymously. Only when the two nodes disconnect with each other, every node sends encrypted encountering evidence to the encountered node to enable encountering data collection. A collection of novel methods are intended to guarantee the confidentiality and uniqueness of encountering proofs. A scalable approach additionally supports fine-grained control over what data is shared to the encountered node in light of attribute similarity (i.e., trust), which is measured without revealing attributes.
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