Privacy leakage via attribute inference in directed social networks

Abstract

Social networking has become a frequent activity for most internet users. Profile attribute inference research has gained popularity due to its importance in social network privacy. While many social networks are in the form of directed networks, most attribute inference approaches are based on undirected networks. Aimed at a directed social network, we propose an algorithm utilising the concepts of tie strength and co-profiling attribute with circles. We propose to infer both attributes and circles iteratively, by propagating the known attribute values of followers and followings within certain circles. With the ability to follow or be followed by any user, the possibility of many weak links being formed is high. We utilize tie-strength to address this and differentiate each user’s influence in the ego user attribute inference. Experiments show the superior performance of our proposed approach over the state of the art method.