Security issues for federated database systems
Computers and Security
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Understanding Terror Networks
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 16th international conference on World Wide Web
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Analysis of terrorist social networks with fractal views
Journal of Information Science
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Analyzing the terrorist social networks with visualization tools
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Identifying implicit relationships between social media users to support social commerce
Proceedings of the 14th Annual International Conference on Electronic Commerce
| Hi-index | 0.00 |
Intelligence and law enforcement force make use of terrorist and criminal social networks to support their investigations such as identifying suspects, terrorist or criminal subgroups, and their communication patterns. Social networks are valuable resources but it is not easy to obtain information to create a complete terrorist or criminal social network. Missing information in a terrorist or criminal social network always diminish the effectiveness of investigation. An individual agency only has a partial terrorist or criminal social network due to their limited information sources. Sharing and integration of social networks between different agencies increase the effectiveness of social network analysis. Unfortunately, information sharing is usually forbidden due to the concern of privacy preservation. In this paper, we introduce the KNN algorithm for subgraph generation and a mechanism to integrate the generalized information to conduct social network analysis. Generalized information such as lengths of the shortest paths, number of nodes on the boundary, and the total number of nodes is constructed for each generalized subgraphs. By utilizing the generalized information shared from other sources, an estimation of distance between nodes is developed to compute closeness centrality. Two experiments have been conducted with random graphs and the Global Salafi Jihad terrorist social network. The result shows that the proposed technique improves the accuracy of closeness centrality measures substantially while protecting the sensitive data.