Privacy Preserving Online Social Networks using Enhanced Equicardinal Clustering

Recent trends show that the popularity of online social networks (OSNs) has been increasing rapidly. From daily communication sites to online communities, an average person's daily life has become dependent on these online networks. Hence, it has become evident that protection should be provided to these networks from unwanted intruders. In this paper, we consider data privacy on online social networks at the network level rather than user level. This network level privacy helps us to prevent information leakage to third-party users like advertisers. We propose a novel scheme that combines both node and edge privacy of the OSN by clustering the similar users. We further improve traditional clustering by utilizing the concept of k-anonymity. Our enhanced equicardinal clustering ensures that there are at least k users in any given network. We further provide proofs on how the proposed equicardinal clustering ensures k-anonymity and the maximum information loss. With the help of two real-world data sets, we evaluate this method in terms of privacy and efficiency.