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 languageEnglish (US)
Title of host publicationHT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media
PublisherAssociation for Computing Machinery, Inc
Pages165-173
Number of pages9
ISBN (Electronic)9781450354271
DOIs
StatePublished - Jul 3 2018
Event29th ACM International Conference on Hypertext and Social Media, HT 2018 - Baltimore, United States
Duration: Jul 9 2018Jul 12 2018

Other

Other29th ACM International Conference on Hypertext and Social Media, HT 2018
CountryUnited States
CityBaltimore
Period7/9/187/12/18

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

Cite this

Beigi, G., Shu, K., Zhang, Y., & Liu, H. (2018). Securing social media user data - An adversarial approach. In HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media (pp. 165-173). Association for Computing Machinery, Inc. https://doi.org/10.1145/3209542.3209552

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 proceedingConference contribution

Beigi, G, Shu, K, Zhang, Y & Liu, H 2018, Securing social media user data - An adversarial approach. in HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media. Association for Computing Machinery, Inc, pp. 165-173, 29th ACM International Conference on Hypertext and Social Media, HT 2018, Baltimore, United States, 7/9/18. https://doi.org/10.1145/3209542.3209552
Beigi G, Shu K, Zhang Y, Liu H. Securing social media user data - An adversarial approach. In HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media. Association for Computing Machinery, Inc. 2018. p. 165-173 https://doi.org/10.1145/3209542.3209552
Beigi, Ghazaleh ; Shu, Kai ; Zhang, Yanchao ; Liu, Huan. / Securing social media user data - An adversarial approach. HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media. Association for Computing Machinery, Inc, 2018. pp. 165-173
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