Proceedings of the 16th international conference on World Wide Web
Measuring Topological Anonymity in Social Networks
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
De-anonymizing Social Networks
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Class-based graph anonymization for social network data
Proceedings of the VLDB Endowment
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
k-symmetry model for identity anonymization in social networks
Proceedings of the 13th International Conference on Extending Database Technology
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Preserving the privacy of sensitive relationships in graph data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Identity obfuscation in graphs through the information theoretic lens
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Privacy-preserving social network publication against friendship attacks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymization of Centralized and Distributed Social Networks by Sequential Clustering
IEEE Transactions on Knowledge and Data Engineering
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Social networks, patient networks, and email networks are all examples of graphs that can be studied to learn about information diffusion, community structure and different system processes; however, they are also all examples of graphs containing potentially sensitive information. While several anonymization techniques have been proposed for social network data publishing, they all apply the anonymization procedure on the entire graph. Instead, we propose a local anonymization algorithm that focuses on obscuring structurally important nodes that are not well anonymized, thereby reducing the cost of the overall anonymization procedure. Based on our experiments, we observe that we reduce the cost of anonymization by an order of magnitude while maintaining, and even improving, the accuracy of different graph centrality measures, e.g. degree and betweenness, when compared to another well known data publishing approach.