Modeling personally identifiable information leakage that occurs through the use of online social networks

M.Sc. (Computer Science)With the phenomenal growth of the Online Social Network (OSN) industry in the past few years, users have resorted to storing vast amounts of personal information on these sites. The information stored on these sites is often readily accessible from anywhere in the world and not always protected by adequate security settings. As a result, user information can make its way, unintentionally, into the hands of not only other online users, but also online abusers. Online abusers, better known as cyber criminals, exploit user information to commit acts of identity theft, Advanced Persistent Threats (APTs) and password recovery, to mention only a few. As OSN users are incapable of visualising the process of access to their OSN information, they may choose to never adjust their security settings. This can become synonymous with ultimately setting themselves up to becoming a victim of cyber crime. In this dissertation we aim to address this problem by proposing a prototype system, the Information Deduction Model (IDM) that can visualise and simulate the process of accessing information on an OSN profile. By visually explaining concepts such as information access, deduction and leakage, we aim to provide users with a tool that will enable them to make more informed choices about the security settings on their OSN profiles thereby setting themselves up for a pleasant online experience