Measuring Privacy Risk in Online Social Networks

Justin Becker and  Hao Chen

Measuring privacy risk in online social networks is a challenging task.  One of the fundamental difficulties is quantifying the amount of information revealed unintentionally.  We present PrivAware, a tool to detect and report unintended information loss in online social networks.  Our goal is to provide a rudimentary framework to identify privacy risk and provide solutions to reduce information loss.  The first instance of the software is focused on information loss attributed to social circles.  In subsequent releases we intend to incorporate additional capabilities to capture ancillary threat models.  From our initial results, we quantify the privacy risk attributed to friend relationships in Facebook.  We show that for each user in our study a majority of their personal attributes can be derived from social contacts.  Moreover, we present results denoting the number of friends contributing to a correctly inferred attribute.  We also provide similar results for different demographics of users. The intent of PrivAware is to not only report information loss but to recommend user actions to mitigate privacy risk.  The actions provide users with the steps necessary to improve their overall privacy measurement.  One obvious, but not ideal, solution is to remove risky friends.  Another approach is to group risky friends and apply access controls to the group to limit visibility.  In summary, our goal is to provide a unique tool to quantify information loss and provide features to reduce privacy risk.