  Online users generate tremendous amounts of data. To better serve users, it
is required to share the user-related data among researchers, advertisers and
application developers. Publishing such data would raise more concerns on user
privacy. To encourage data sharing and mitigate user privacy concerns, a number
of anonymization and de-anonymization algorithms have been developed to help
protect privacy of users. This paper reviews my doctoral research on online
users privacy specifically in social media. In particular, I propose a new
adversarial attack specialized for social media data. I further provide a
principled way to assess effectiveness of anonymizing different aspects of
social media data. My work sheds light on new privacy risks in social media
data due to innate heterogeneity of user-generated data.
