  In this paper, we develop a user-centric privacy framework for quantitatively
assessing the exposure of personal information in open settings. Our
formalization addresses key-challenges posed by such open settings, such as the
unstructured dissemination of heterogeneous information and the necessity of
user- and context-dependent privacy requirements. We propose a new definition
of information sensitivity derived from our formalization of privacy
requirements, and, as a sanity check, show that hard non-disclosure guarantees
are impossible to achieve in open settings.
  After that, we provide an instantiation of our framework to address the
identity disclosure problem, leading to the novel notion of d-convergence.
d-convergence is based on indistinguishability of entities and it bounds the
likelihood with which an adversary successfully links two profiles of the same
user across online communities.
  Finally, we provide a large-scale evaluation of our framework on a collection
of 15 million comments collected from the Online Social Network Reddit. Our
evaluation validates the notion of d-convergence for assessing the linkability
of entities in our data set and provides deeper insights into the data set's
structure.
