Exploring rationality of self awareness in social networking for logical modeling of unintentional insiders
This addresses privacy risks for social media users by formalizing unintentional insider threats, though it is incremental as it builds on existing frameworks.
The study tackled the problem of users oversharing personal information on social media due to privacy unawareness and approval-seeking motivations, by developing a tool to reveal publicly known data and testing if it changes behavior, with results showing it helps model unintentional insiders in a formal framework.
Unawareness of privacy risks together with approval seeking motivations make humans enter too much detail into the likes of Facebook, Twitter, and Instagram. To test whether the rationality principle applies, we construct a tool that shows to a user what is known publicly on social networking sites about her. In our experiment, we check whether this revelation changes human behaviour. To extrapolate and generalize, we use the insights gained by practical experimentation. Unaware users can become targeted by attackers. They then become unintentional insiders. We demonstrate this by extending the Isabelle Insider framework to accommodate a formal model of unintentional insiders, an open problem with long standing.