  The amount of personal information unwillingly exposed by users on online
social networks is staggering, as shown in recent research. Moreover, recent
reports indicate that these networks are infested with tens of millions of fake
users profiles, which may jeopardize the users' security and privacy. To
identify fake users in such networks and to improve users' security and
privacy, we developed the Social Privacy Protector software for Facebook. This
software contains three protection layers, which improve user privacy by
implementing different methods. The software first identifies a user's friends
who might pose a threat and then restricts this "friend's" exposure to the
user's personal information. The second layer is an expansion of Facebook's
basic privacy settings based on different types of social network usage
profiles. The third layer alerts users about the number of installed
applications on their Facebook profile, which have access to their private
information. An initial version of the Social Privacy Protection software
received high media coverage, and more than 3,000 users from more than twenty
countries have installed the software, out of which 527 used the software to
restrict more than nine thousand friends. In addition, we estimate that more
than a hundred users accepted the software's recommendations and removed at
least 1,792 Facebook applications from their profiles. By analyzing the unique
dataset obtained by the software in combination with machine learning
techniques, we developed classifiers, which are able to predict which Facebook
profiles have high probabilities of being fake and therefore, threaten the
user's well-being. Moreover, in this study, we present statistics on users'
privacy settings and statistics of the number of applications installed on
Facebook profiles...
