  Social media 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 social media users. In this work, we
propose a new adversarial attack specialized for social media data. We further
provide a principled way to assess effectiveness of anonymizing different
aspects of social media data. Our work sheds light on new privacy risks in
social media data due to innate heterogeneity of user-generated data which
require striking balance between sharing user data and protecting user privacy.
