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 kw-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 kw-structural diversity anonymity so that the
adversary cannot utilize his knowledge to re identify 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.
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