document: Privacy in dynamic social networks 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 .
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