The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 16th international conference on World Wide Web
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
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
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
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
Anonymizing bipartite graph data using safe groupings
The VLDB Journal — The International Journal on Very Large Data Bases
k-symmetry model for identity anonymization in social networks
Proceedings of the 13th International Conference on Extending Database Technology
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Sensitive label privacy protection on social network data
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
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You are on Facebook or you are out. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many of us, from the reason behind our joining social media and publishing and sharing details of our professional and private lives. Not only the personal details we may reveal but also the very structure of the networks themselves are sources of invaluable information for any organization wanting to understand and learn about social groups, their dynamics and their members. These organizations may or may not be benevolent. It is therefore important to devise, design and evaluate solutions that guarantee some privacy. One approach that attempts to reconcile the different stakeholders' requirement is the publication of a modified graph. The perturbation is hoped to be sufficient to protect members' privacy while it maintains sufficient utility for analysts wanting to study the social media as a whole. It is necessarily a compromise. In this paper we try and empirically quantify the inevitable trade-off between utility and privacy. We do so for one state-of-the-art graph anonymization algorithm that protects against most structural attacks, the k-automorphism algorithm. We measure several metrics for a series of real graphs from various social media before and after their anonymization under various settings.