Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata. (arXiv:2204.13734v1 [cs.CR])

We present a systematic refactoring of the conventional treatment of privacy
analyses, basing it on mathematical concepts from the framework of Quantitative
Information Flow (QIF). The approach we suggest brings three principal
advantages: it is flexible, allowing for precise quantification and comparison
of privacy risks for attacks both known and novel; it can be computationally
tractable for very large, longitudinal datasets; and its results are
explainable both to politicians and to the general public. We apply our
approach to a …

application assessment datasets large privacy