The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Proceedings of the 16th international conference on World Wide Web
Growth of the flickr social network
Proceedings of the first workshop on Online social networks
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A brief survey on anonymization techniques for privacy preserving publishing of social network data
ACM SIGKDD Explorations Newsletter
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
Privacy-aware data management in information networks
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
A secret sharing based privacy enforcement mechanism for untrusted social networking operators
MiFor '11 Proceedings of the 3rd international ACM workshop on Multimedia in forensics and intelligence
Discretionary social network data revelation with a user-centric utility guarantee
Proceedings of the 21st ACM international conference on Information and knowledge management
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Anonymization of social networks before they are published or shared has become an important research question. Recent work on anonymizing social networks has looked at privacy preserving techniques for publishing a single instance of the network. However, social networks evolve and a single instance is inadequate for analyzing the evolution of the social network or for performing any longitudinal data analysis. We study the problem of repeatedly publishing social network data as the network evolves, while preserving privacy of users. Publishing multiple instances of the same network independently has privacy risks, since stitching the information together may allow an adversary to identify users in the networks. We propose methods to anonymize a dynamic network such that the privacy of users is preserved when new nodes and edges are added to the published network. These methods make use of link prediction algorithms to model the evolution of the social network. Using this predicted graph to perform group-based anonymization, the loss in privacy caused by new edges can be reduced. We evaluate the privacy loss on publishing multiple social network instances using our methods.