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Online Social Networks (OSNs), such as Facebook, provide users with tools toshare information along with a set of privacy controls preferences to regulatethe spread of information. Current privacy controls are efficient to protectcontent data. However, the complexity of tuning them undermine their efficiencywhen protecting contextual information (such as the social network structure)that many users believe being kept private.  In this paper, we demonstrate the extent of the problem of informationleakage in Facebook. In particular, we show the possibility of inferring, fromthe network "surrounding" a victim user, some information that the victim setas hidden. We developed a system, named OSSINT (Open Source Social NetworkINTelligence), on top of our previous tool SocialSpy, that is able to inferhidden information of a victim profile and retrieve private information frompublic one. OSSINT retrieves the friendship network of a victim and shows howit is possible to infer additional private information (e.g., user personalpreferences and hobbies). Our proposed system OSSINT goes extra mile about thenetwork topology information, i.e., predicting new friendships using thevictim's friends of friends network (2-hop of distance from the victimprofile), and hence possibly deduce private information of the full Facebooknetwork. OSSINT correctly improved the previous results of SocialSpy predictingan average of 11 additional friendships with peaks of 20 new friends. Moreover,OSSINT, for the considered victim profiles demonstrated how it is possible toinfer real life information such as current city, hometown, university,supposed being private.

OSSINT - Open Source Social Network Intelligence An efficient and effective way to uncover "private" information in OSN profiles