From Zoos to Safaris--From Closed-World Enforcement to Open-World Assessment of Privacy

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 necessity of user- and context-dependent privacy requirements. As a sanity check, we show that hard non-disclosure guarantees are impossible to achieve in open settings. In the second part, we provide an instantiation of our framework to address the identity disclosure problem, leading to the novel notion of d-convergence to assess the linkability of identities across online communities. Since user-generated text content plays a major role in linking identities between Online Social Networks, we further extend this linkability model to assess the effectiveness of countermeasures against linking authors of text content by their writing style. We experimentally evaluate both of these instantiations by applying them to suitable data sets: we provide a large-scale evaluation of the linkability model on a collection of 15 million comments collected from the Online Social Network Reddit, and evaluate the effectiveness of four semantics-retaining countermeasures and their combinations on the Extended-Brennan-Greenstadt Adversarial Corpus. Through these evaluations we validate the notion of d-convergence for assessing the linkability of entities in our Reddit data set and explore the practical impact of countermeasures on the importance of standard writing style features on identifying authors.