| Title | Securing Social Media User Data: An Adversarial Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Beigi, Ghazaleh, Shu, Kai, Zhang, Yanchao, Liu, Huan |
| Conference Name | Proceedings of the 29th on Hypertext and Social Media |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5427-1 |
| Keywords | composability, data sharing, data utility, de-anonymization attack, heterogeneous data, Human Behavior, Metrics, pubcrawl, relational database security, Resiliency, social network analysis, user privacy |
| Abstract | 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. |
| URL | http://doi.acm.org/10.1145/3209542.3209552 |
| DOI | 10.1145/3209542.3209552 |
| Citation Key | beigi_securing_2018 |
Securing Social Media User Data: An Adversarial Approach