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2010
ACM

Privacy in dynamic social networks

9 years 11 months ago
Privacy in dynamic social networks
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. We 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. We provide methods to anonymize a dynamic network when new nodes and edges are added to the published network. These methods use link prediction algorithms to model the evolution. Using this predicted graph to perform group-based anonymization, the loss in privacy caused by new edges can be eliminated almost entirely. We propose metrics for privacy loss, and evaluate them for publishing multiple OSN instances.
Smriti Bhagat, Graham Cormode, Balachander Krishna
Added 03 Jul 2010
Updated 03 Jul 2010
Type Conference
Year 2010
Where WWW
Authors Smriti Bhagat, Graham Cormode, Balachander Krishnamurthy, Divesh Srivastava
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