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  1. Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics (CSI-KDD '09)
  2. Social networks integration and privacy preservation using subgraph generalization
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AntiPhish: lessons learnt
Combining incremental Hidden Markov Model and Adaboost algorithm for anomaly intrusion detection
Data security and integrity: developments and directions
Towards trusted intelligence information sharing
Addressing the attack attribution problem using knowledge discovery and multi-criteria fuzzy decision-making
Social networks integration and privacy preservation using subgraph generalization
Malware detection using statistical analysis of byte-level file content
Design of a temporal geosocial semantic web for military stabilization and reconstruction operations
Online phishing classification using adversarial data mining and signaling games
On the efficacy of data mining for security applications
A study of online service and information exposure of public companies

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Social networks integration and privacy preservation using subgraph generalization

Content Provider ACM Digital Library
Author Yang, Christopher C. Tang, Xuning
Abstract 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.
Starting Page 53
Ending Page 61
Page Count 9
File Format PDF
ISBN 9781605586694
DOI 10.1145/1599272.1599284
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-06-28
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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