Prediction Promotes Privacy in Dynamic Social Networks

Date Added: May 2010
Format: PDF

Recent work on anonymizing Online Social Networks (OSNs) has looked at privacy preserving techniques for publishing a single instance of the network. However, OSNs evolve and a single instance is inadequate for analyzing their evolution or performing longitudinal data analysis. The authors study the problem of repeatedly publishing OSN data as the network evolves while preserving privacy of users. Publishing multiple instances independently has privacy risks, since stitching the information together may allow an adversary to identify users. They provide methods to anonymize a dynamic network when new nodes and edges are added to the published network.