securing social media user data an adversarial approach -- 2

its about anonymizing and de-anonymizing(algorithms) social media(Twitter) user's data, then attacking the anonymized data to evaluate if theirs security leakage on the anonymized users data. using twitter(API) user's dataset AND Foursquare (API) for users location. coding in python.

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.