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.
| Original language | English (US) |
|---|---|
| Title of host publication | HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 165-173 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781450354271 |
| DOIs | |
| State | Published - Jul 3 2018 |
| Event | 29th ACM International Conference on Hypertext and Social Media, HT 2018 - Baltimore, United States Duration: Jul 9 2018 → Jul 12 2018 |
Other
| Other | 29th ACM International Conference on Hypertext and Social Media, HT 2018 |
|---|---|
| Country | United States |
| City | Baltimore |
| Period | 7/9/18 → 7/12/18 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Human-Computer Interaction
- Computer Graphics and Computer-Aided Design
Cite this
Securing social media user data - An adversarial approach. / Beigi, Ghazaleh; Shu, Kai; Zhang, Yanchao; Liu, Huan.
HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media. Association for Computing Machinery, Inc, 2018. p. 165-173.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Securing social media user data - An adversarial approach
AU - Beigi, Ghazaleh
AU - Shu, Kai
AU - Zhang, Yanchao
AU - Liu, Huan
PY - 2018/7/3
Y1 - 2018/7/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85051560711&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051560711&partnerID=8YFLogxK
U2 - 10.1145/3209542.3209552
DO - 10.1145/3209542.3209552
M3 - Conference contribution
AN - SCOPUS:85051560711
SP - 165
EP - 173
BT - HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media
PB - Association for Computing Machinery, Inc
ER -