Sharing-habits based privacy control in social networks

We study users behavior in online social networks (OSN) as a means to preserve privacy. People widely use OSN for a variety of objectives and fields. Each OSN has different characteristics, requirements, and vulnerabilities of the private data shared. Sharing-habits refers to users' patterns of sharing information. Sharing-habits are implied by the communication between users and their peers. While social networks allow users to have some control over the dissemination of their information, most users are not aware that the private information they share might leak to users with whom they do not wish to share it. In this paper we address the growing need of social network users to share information with close fiends while hiding it from others. We apply several different well-known strategies from graph-flow theory to an OSN graph with sharing-habits insights, to control the information flow among OSN users. The goal of the method we present is to allow maximum information sharing while enforcing a user's pre-defined privacy criteria. Our method is evaluated using real data from well known social networks and the results are analyzed in terms of accuracy and run-time.