  Matching the profiles of a user across multiple online social networks brings
opportunities for new services and applications as well as new insights on user
online behavior, yet it raises serious privacy concerns. Prior literature has
proposed methods to match profiles and showed that it is possible to do it
accurately, but using evaluations that focused on sampled datasets only. In
this paper, we study the extent to which we can reliably match profiles in
practice, across real-world social networks, by exploiting public attributes,
i.e., information users publicly provide about themselves. Today's social
networks have hundreds of millions of users, which brings completely new
challenges as a reliable matching scheme must identify the correct matching
profile out of the millions of possible profiles. We first define a set of
properties for profile attributes--Availability, Consistency,
non-Impersonability, and Discriminability (ACID)--that are both necessary and
sufficient to determine the reliability of a matching scheme. Using these
properties, we propose a method to evaluate the accuracy of matching schemes in
real practical cases. Our results show that the accuracy in practice is
significantly lower than the one reported in prior literature. When considering
entire social networks, there is a non-negligible number of profiles that
belong to different users but have similar attributes, which leads to many
false matches. Our paper sheds light on the limits of matching profiles in the
real world and illustrates the correct methodology to evaluate matching schemes
in realistic scenarios.
