Information Integration for Terrorist or Criminal Social Networks

Abstract

Social network analysis discovers knowledge embedded in the structure of social networks, which is useful for intelligence and law enforcement force in investigation. However, individual agency usually has part of the complete terrorist or criminal social network and therefore some crucial knowledge could not be extracted. Sharing information between different agencies will make such a social network analysis more effective, unfortunately the concern of privacy preservation usually prohibits the sharing of sensitive information. There is always a trade-off between the degree of privacy and the degree of utility in information sharing. Several approaches have been proposed to resolve such dilemma in sharing data from different relational tables. However, there is only limited amount of work on sharing social networks from different sources and yet trying to minimize the reduction on the degree of privacy. The work on privacy preservation of social network data relies on anonymity and perturbation. These techniques are developed for the purpose of data publishing, but ignore the utility of the published data on social network analysis and the integration of social networks from multiple sources. In this chapter, we propose a sub-graph generalization approach for information sharing and privacy preservation of terrorist or criminal social networks. The objectives are sharing the insensitive and generalized information to support social network analysis but preserving the privacy at the same time. Our experiment shows that such an approach is promising.