  Social media has drastically reshaped the world that allows billions of
people to engage in such interactive environments to conveniently create and
share content with the public. Among them, text data (e.g., tweets, blogs)
maintains the basic yet important social activities and generates a rich source
of user-oriented information. While those explicit sensitive user data like
credentials has been significantly protected by all means, personal private
attribute (e.g., age, gender, location) disclosure due to inference attacks is
somehow challenging to avoid, especially when powerful natural language
processing (NLP) techniques have been effectively deployed to automate
attribute inferences from implicit text data. This puts users' attribute
privacy at risk. To address this challenge, in this paper, we leverage the
inherent vulnerability of machine learning to adversarial attacks, and design a
novel text-space Adversarial attack for Social Good, called Adv4SG. In other
words, we cast the problem of protecting personal attribute privacy as an
adversarial attack formulation problem over the social media text data to
defend against NLP-based attribute inference attacks. More specifically, Adv4SG
proceeds with a sequence of word perturbations under given constraints such
that the probed attribute cannot be identified correctly. Different from the
prior works, we advance Adv4SG by considering social media property, and
introducing cost-effective mechanisms to expedite attribute obfuscation over
text data under the black-box setting. Extensive experiments on real-world
social media datasets have demonstrated that our method can effectively degrade
the inference accuracy with less computational cost over different attribute
settings, which substantially helps mitigate the impacts of inference attacks
and thus achieve high performance in user attribute privacy protection.
