  The increasing popularity of online social network brings huge privacy threat
for the end users. While existing work focus on inferring sensitive attributes
from the social network such as age, location and gender, little has been done
on how to protect the users' privacy by preventing the malicious inference. In
this paper we investigated the privacy vulnerability of the existing social
network and designed a privacy-preserving framework. We evaluated the
framework's privacy and usefulness guarantees, demonstrated its effectiveness
on classification and the defense against the privacy attack.
