  The problem of online privacy is often reduced to individual decisions to
hide or reveal personal information in online social networks (OSNs). However,
with the increasing use of OSNs, it becomes more important to understand the
role of the social network in disclosing personal information that a user has
not revealed voluntarily: How much of our private information do our friends
disclose about us, and how much of our privacy is lost simply because of online
social interaction? Without strong technical effort, an OSN may be able to
exploit the assortativity of human private features, this way constructing
shadow profiles with information that users chose not to share. Furthermore,
because many users share their phone and email contact lists, this allows an
OSN to create full shadow profiles for people who do not even have an account
for this OSN.
  We empirically test the feasibility of constructing shadow profiles of sexual
orientation for users and non-users, using data from more than 3 Million
accounts of a single OSN. We quantify a lower bound for the predictive power
derived from the social network of a user, to demonstrate how the
predictability of sexual orientation increases with the size of this network
and the tendency to share personal information. This allows us to define a
privacy leak factor that links individual privacy loss with the decision of
other individuals to disclose information. Our statistical analysis reveals
that some individuals are at a higher risk of privacy loss, as prediction
accuracy increases for users with a larger and more homogeneous first- and
second-order neighborhood of their social network. While we do not provide
evidence that shadow profiles exist at all, our results show that disclosing of
private information is not restricted to an individual choice, but becomes a
collective decision that has implications for policy and privacy regulation.
