A centrality-based measure of user privacy in online social networks

The risks due to a global and unaware diffusion of our personal data cannot be overlooked when more than two billion people are estimated to be registered in at least one of the most popular online social networks. As a consequence, privacy has become a primary concern among social network analysts and Web/data scientists. Some studies propose to “measure” users' profile privacy according to their privacy settings but do not consider the topological properties of the social network adequately. In this paper, we address this limitation and define a centrality-based privacy score to measure the objective user privacy risk according to the network properties. We analyze the effectiveness of our measures on a large network of real Facebook users.