Generalizing terrorist social networks with K-nearest neighbor and edge betweeness for social network integration and privacy preservation

Social network analysis has been shown to be effective in supporting intelligence and law enforcement force to identify suspects, terrorist or criminal subgroups, and their communication patterns. However, social network data owned by individual law enforcement units contain private information that must be preserved before sharing with other law enforcement units. Such privacy issue tremendously reduces the utility of the social network data since the integration of social networks from different law enforcement units cannot be fully integrated. Without integration of social network data, the effectiveness of terrorist or criminal social network analysis is diminished. In this paper, we introduce the KNN and EBB algorithm for constructing generalized subgraphs and a mechanism to integrate the generalized information to conduct the closeness centrality measures. The result shows that the proposed technique improves the accuracy of closeness centrality measures substantially while protecting the sensitive data.