  Online Social Networking Sites attracted a massive number of users over the
past decade but also raised privacy concerns with the amount of personal
information disclosed. Studies have shown that 25% of the users are not aware
of privacy settings provided by these sites or do not know how to change them.
This paper investigates an approach towards understanding users' privacy
behavior on social media, e.g. Facebook, through studying faces, tags and photo
privacy settings. It classifies users based on their privacy selections and
proposes a system for monitoring and recommending stronger privacy settings. An
application is developed, and our case study examines the effectiveness of our
model.
