  Real network datasets provide significant benefits for understanding
phenomena such as information diffusion or network evolution. Yet the privacy
risks raised from sharing real graph datasets, even when stripped of user
identity information, are significant. When nodes have associated attributes,
the privacy risks increase. In this paper we quantitatively study the impact of
binary node attributes on node privacy by employing machine-learning-based
re-identification attacks and exploring the interplay between graph topology
and attribute placement. Our experiments show that the population's diversity
on the binary attribute consistently degrades anonymity.
