Exploring social network privacy measurement using fuzzy vector commitment

Social network is an exciting online community for sharing the moments, opinion and comments. This tendency of having a unanimous person as a friend or as an acquaintance could be a serious threat to the security aspects of social network and the privacy and confidentiality of information may also become vulnerable. Several techniques are available to measure security and sustain the privacy in the current literature. In this paper, social network is considered as a vector space with multi dimensions and any community within this network is considered as a vector subspace of the social network. A Fuzzy Commitment Vector is introduced to provide high level privacy using dimension reduction of subspace in the social network. As the results indicate, the Fuzzy Commitment Vector provides a significantly higher level of privacy protection compared to some of the existing techniques cited in the literature.