  Most previous works in privacy of Online Social Networks (OSN) focus on a
restricted scenario of using one type of information to infer another type of
information or using only static profile data such as username, profile picture
or home location. However the multimedia footprints of users has become
extremely diverse nowadays. In reality, an adversary would exploit all types of
information obtainable over time, to achieve its goal. In this paper, we
analyse OSN privacy by jointly exploiting longterm multimodal information. We
focus in particular on inference of social relationships. We consider five
popular components of posts shared by users, namely images, hashtags, captions,
geo-locations and published friendships. Large scale evaluation on a real-world
OSN dataset shows that while our monomodal attacks achieve strong predictions,
our multimodal attack leads to a stronger performance with AUC (area under the
ROC curve) above 0.9. Our results highlight the need for multimodal obfuscation
approaches towards protecting privacy in an era where multimedia footprints of
users get increasingly diverse.
