Abstract: Nowadays, online social network data are being increasingly published to third parties. It has been shown that individually sensitive information can be recovered from the released data and several anonymization techniques have been proposed. However, most of these defenses have focused on “one-time” releases and do not take into account the re-publication of dynamic social network data. Re-publishing data periodically is a natural result of social network evolution and an emerging requirement of dynamic social network analysis. In this paper, we show that by utilizing correlations between sequential releases, the adversary can achieve high precision in de-anonymization of the released data, suppressing the uncertainty of re-identifying each release separately and synthesizing the results afterwards. Besides, we combine structural knowledge with node attributes to compromise graph modification based defenses. With experiments on real data, this work is the first to demonstrate feasibility of de-anonymizing dynamic social networks and should arouse concern for future works on privacy preservation in social network data publishing.