Unvilling In Twitter Inference Attack On Browsing History Of Twittwe Users Using Public Click Analytics And Twitter Metadata

Abstract

Twitter is a popular online social network
service for sharing short messages (tweets)
among friends. Its users frequently use URL
shortening services that provide (i) a short
alias of a long URL for sharing it via tweets
and (ii) public click analytics of shortened
URLs. The public click analytics is provided
in an aggregated form to preserve the privacy
of individual users. In this paper, we propose
practical attack techniques inferring who
clicks which shortened URLs on Twitter
using the combination of public information:
Twitter metadata and public click analytics.
Unlike the conventional browser history
stealing attacks, our attacks only demand
publicly available information provided by
Twitter and URL shortening services.
Evaluation results show that our attack can
compromise Twitter users’ privacy with high
accuracy. While these platforms offer
unprecedented opportunities for connectivity
and information sharing, they have also
become fertile ground for the rapid
dissemination of misinformation. This
erosion of information authenticity poses a
significant challenge to network space
governance and underscores the critical need
for establishing a trusted online environment.
In response to this growing concern, this
study delves into a novel problem termed the
Activity Minimization of Misinformation
Influence (AMMI) problem. The core
objective of the AMMI problem