Detecting Overlapping Communities in LBSNs with Enhanced Location Privacy

Abstract

Location based social network (LBSNs) for instance Facebook places and Twitter provides large amount of data which allows service providers to create several applications like group marketing, friend and location recommendations, trend inquiry etc. Location based social networks does not provide precise communities which enables users to subscribe, join or follow. Strengthened community disclosure is needed for capitalizing probable users. The variance of user's behavior and preferences makes those communities to overlie. Several procedures have been introduced for detecting overlapping communities in LBSNs. Still a severe threat to LBSNs is that location data may be misused to steal their identity track users and execute home invasions and even stalk or intimidate them. This is a framework to discover overlapping communities based on users check-ins at various locations and user venue attributes, with refined location privacy by secure use-specific coordinate conversations to location data which will be shared with the server by preserving the distance.