Privacy preserving text representation learning

Ghazaleh Beigi, Kai Shu, Ruocheng Guo, Suhang Wang, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Online users generate tremendous amounts of textual information by participating in different online activities. This data provides opportunities for researchers and business partners to understand individuals. However, this user-generated textual data not only can reveal the identity of the user but also may contain individual's private attribute information. Publishing the textual data thus compromises the privacy of users. It is challenging to design effective anonymization techniques for textual information which minimize the chances of re-identification and does not contain private information while retaining the textual semantic meaning. In this paper, we study this problem and propose a novel double privacy preserving text representation learning framework, DPText. We show the effectiveness of DPText in preserving privacy and utility.

Original languageEnglish (US)
Title of host publicationHT 2019 - Proceedings of the 30th ACM Conference on Hypertext and Social Media
PublisherAssociation for Computing Machinery, Inc
Pages275-276
Number of pages2
ISBN (Electronic)9781450368858
DOIs
StatePublished - Sep 12 2019
Event30th ACM Conference on Hypertext and Social Media, HT 2019 - Hof, Germany
Duration: Sep 17 2019Sep 20 2019

Publication series

NameHT 2019 - Proceedings of the 30th ACM Conference on Hypertext and Social Media

Conference

Conference30th ACM Conference on Hypertext and Social Media, HT 2019
CountryGermany
CityHof
Period9/17/199/20/19

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Privacy preserving text representation learning'. Together they form a unique fingerprint.

  • Cite this

    Beigi, G., Shu, K., Guo, R., Wang, S., & Liu, H. (2019). Privacy preserving text representation learning. In HT 2019 - Proceedings of the 30th ACM Conference on Hypertext and Social Media (pp. 275-276). (HT 2019 - Proceedings of the 30th ACM Conference on Hypertext and Social Media). Association for Computing Machinery, Inc. https://doi.org/10.1145/3342220.3344925