securing social media users data An Adversarial approach
Budget: $30 – $250 USD
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.
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.