  Using real-world study data usually requires contractual agreements where
research results may only be published in anonymized form. Requiring formal
privacy guarantees, such as differential privacy, could be helpful for
data-driven projects to comply with data protection. However, deploying
differential privacy in consumer use cases raises the need to explain its
underlying mechanisms and the resulting privacy guarantees. In this paper, we
thoroughly review and extend an existing privacy metric. We show how to compute
this risk metric efficiently for a set of basic statistical queries. Our
empirical analysis based on an extensive, real-world scientific data set
expands the knowledge on how to compute risks under realistic conditions, while
presenting more challenges than solutions.
