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
Number of pages9
ISBN (Electronic)9781450354271
StatePublished - Jul 3 2018
Event29th ACM International Conference on Hypertext and Social Media, HT 2018 - Baltimore, United States
Duration: Jul 9 2018Jul 12 2018


Other29th ACM International Conference on Hypertext and Social Media, HT 2018
CountryUnited States

ASJC Scopus subject areas

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

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