Securing Social Media User Data - An Adversarial Approach
This work addresses privacy concerns for social media users and data handlers, but it is incremental as it builds on existing anonymization and de-anonymization algorithms.
The paper tackles the problem of privacy risks in social media data sharing by proposing a new adversarial attack specialized for such data and providing a principled assessment method for anonymization effectiveness, highlighting the need to balance data sharing with privacy protection.
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.