Personal Privacy Metric based on Public Social Network Data

Abstract

Attackers can exploit the published data of the social network by the technology of big data analysis to find the user's privacy without any permission or friendship. In this paper, to solve the problem of privacy metric under the undefined background knowledge, we derive a new metric to quantify the privacy in the complex network circumstances which is inspired by a theory named set pair analysis, meanwhile we put forward a privacy measurement model based on that. At last, we carry out an experiment with the open data in social network. The result shows that the proposed method can achieve the goal of privacy measures under the uncertain background knowledge.