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Issue No. 03 - March (2014 vol. 26)
ISSN: 1041-4347
pp: 635-651
Chih-Hua Tai , National Taipei University, Taipei
Peng-Jui Tseng , National Taiwan University, Taipei
Philip S. Yu , University of Illinois at Chicago, Chicago
Ming-Syan Chen , Academia Sinica, Taipei
Social networks model the social activities between individuals, which change as time goes by. In light of useful information from such dynamic networks, there is a continuous demand for privacy-preserving data sharing with analyzers, collaborators or customers. In this paper, we address the privacy risks of identity disclosures in sequential releases of a dynamic network. To prevent privacy breaches, we proposed novel $(k^w)$-structural diversity anonymity, where $(k)$ is an appreciated privacy level and $(w)$ is a time period that an adversary can monitor a victim to collect the attack knowledge. We also present a heuristic algorithm for generating releases satisfying $(k^w)$-structural diversity anonymity so that the adversary cannot utilize his knowledge to reidentify the victim and take advantages. The evaluations on both real and synthetic data sets show that the proposed algorithm can retain much of the characteristics of the networks while confirming the privacy protection.
Privacy, Communities, Heuristic algorithms, Diseases, Data privacy, Educational institutions, Electronic mail

C. Tai, P. Tseng, P. S. Yu and M. Chen, "Identity Protection in Sequential Releases of Dynamic Networks," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 3, pp. 635-651, 2014.
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