  In this work, we propose a profile matching (or deanonymization) attack for
unstructured online social networks (OSNs) in which similarity in graphical
structure cannot be used for profile matching. We consider different attributes
that are publicly shared by users. Such attributes include both obvious
identifiers such as the user name and non-obvious identifiers such as interest
similarity or sentiment variation between different posts of a user in
different platforms. We study the effect of using different combinations of
these attributes to the profile matching in order to show the privacy threat in
an extensive way. Our proposed framework mainly relies on machine learning
techniques and optimization algorithms. We evaluate the proposed framework on
two real-life datasets that are constructed by us. Our results indicate that
profiles of the users in different OSNs can be matched with high probability by
only using publicly shared attributes and without using the underlying
graphical structure of the OSNs. We also propose possible countermeasures to
mitigate this threat in the expense of reduction in the accuracy (or utility)
of the attributes shared by the users. We formulate the tradeoff between the
privacy and profile utility of the users as an optimization problem and show
how slight changes in the profiles of the users would reduce the success of the
attack. We believe that this work will be a valuable step to build a
privacy-preserving tool for users against profile matching attacks between
OSNs.
