  Operators of online social networks are increasingly sharing potentially
sensitive information about users and their relationships with advertisers,
application developers, and data-mining researchers. Privacy is typically
protected by anonymization, i.e., removing names, addresses, etc.
  We present a framework for analyzing privacy and anonymity in social networks
and develop a new re-identification algorithm targeting anonymized
social-network graphs. To demonstrate its effectiveness on real-world networks,
we show that a third of the users who can be verified to have accounts on both
Twitter, a popular microblogging service, and Flickr, an online photo-sharing
site, can be re-identified in the anonymous Twitter graph with only a 12% error
rate.
  Our de-anonymization algorithm is based purely on the network topology, does
not require creation of a large number of dummy "sybil" nodes, is robust to
noise and all existing defenses, and works even when the overlap between the
target network and the adversary's auxiliary information is small.
