Information sharing and privacy protection of terrorist or criminal social networks

Abstract

Terrorist or criminal social network analysis is helpful 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 is not able to be extracted. Sharing information between different agencies will make such social network analysis more effective; unfortunately, it may violate the privacy of some sensitive information. There is always a tradeoff 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. There is not any work on sharing social networks from different sources and yet try to minimize the reduction on the degree of privacy. In this paper, we propose a subgraph generalization approach for information sharing and privacy protection of terrorist or criminal social networks. Our experiment shows that such approach is promising.