A Novel (K, X)-isomorphism Method for Protecting Privacy in Weighted social Network

From the beginning of 21st century, most people, especially the young ones, used to share whatever they want from their stuff like photo, chats, opinion, interests, accomplishments, and so on, over the social network day after day. One of the quite popular debates is about that if the social network sites preserve the individual privacy or not. The anonymizing techniques are famous techniques which provide privacy preservation for the published structural data. The proposed method aims to preserve the individuals’ privacy in the weighted social network network. This research proposes a (K, X)-isomorphism method, which is an anonymizing technique that produces for every subgraph a K -1 candidate subgraph. A (K X)-isomorphism depends on a range of methods that will help to make for every subgraph a ${K-1}$ similar subgraphs, like weighted community detection, graph density, weighted maximum common subgraph and bi-clustering methods. This research improves an MPD\_V method which is a maximum common subgraph, where this improvement makes MPD\_V more fitting to find the similarly weighted subgraphs.