Social Media and User Privacy
It tackles privacy concerns for social media users, but appears incremental as it builds on existing anonymization and de-anonymization research.
This paper addresses privacy risks in social media data by proposing a new adversarial attack specialized for such data and providing a principled method to assess anonymization effectiveness, highlighting risks due to data heterogeneity.
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.