  We propose new privacy attacks to infer attributes (e.g., locations,
occupations, and interests) of online social network users. Our attacks
leverage seemingly innocent user information that is publicly available in
online social networks to infer missing attributes of targeted users. Given the
increasing availability of (seemingly innocent) user information online, our
results have serious implications for Internet privacy -- private attributes
can be inferred from users' publicly available data unless we take steps to
protect users from such inference attacks.
  To infer attributes of a targeted user, existing inference attacks leverage
either the user's publicly available social friends or the user's behavioral
records (e.g., the webpages that the user has liked on Facebook, the apps that
the user has reviewed on Google Play), but not both. As we will show, such
inference attacks achieve limited success rates. However, the problem becomes
qualitatively different if we consider both social friends and behavioral
records. To address this challenge, we develop a novel model to integrate
social friends and behavioral records and design new attacks based on our
model. We theoretically and experimentally demonstrate the effectiveness of our
attacks. For instance, we observe that, in a real-world large-scale dataset
with 1.1 million users, our attack can correctly infer the cities a user lived
in for 57% of the users, via confidence estimation, we are able to increase the
attack success rate to over 90% if the attacker selectively attacks a half of
the users. Moreover, we show that our attack can correctly infer attributes for
significantly more users than previous attacks.
