Turning attacks into protection: Social media privacy protection using adversarial attacks

Xiaoting Li, Lingwei Chen, Dinghao Wu

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

12 Scopus citations

Abstract

Machine learning, especially deep learning, has emerged as one of the most powerful tools for attribute inference attacks over social media, which poses serious threats to users’ privacy and security. In this paper, we explore a novel perspective of protecting data privacy in social media, where we take advantage of the vulnerability of machine learning, and introduce adversarial attacks to forge latent feature representations and mislead attribute inference attacks. Considering that text data in social media shares the most significant privacy of users, we investigate how text-space adversarial attacks can be elaborated to obfuscate users’ attributes, and accordingly present a text-space adversarial attack as defense, or AaaD for short. Specifically, we advance AaaD by constructing semantically and visually similar word candidates to perturb, and leveraging word importance scores as selection probabilities to upgrade a population-based optimization to expedite adversarial text generation. We evaluate the performance of AaaD on two social media data sets, while the experimental results validate its effectiveness against inference attacks. Our work yields great value and unveils a new insight on the applicability of adversarial attacks for attribute obfuscation and privacy protection.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining, SDM 2021
PublisherSiam Society
Pages208-216
Number of pages9
ISBN (Electronic)9781611976700
StatePublished - 2021
Event2021 SIAM International Conference on Data Mining, SDM 2021 - Virtual, Online
Duration: Apr 29 2021May 1 2021

Publication series

NameSIAM International Conference on Data Mining, SDM 2021

Conference

Conference2021 SIAM International Conference on Data Mining, SDM 2021
CityVirtual, Online
Period4/29/215/1/21

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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