  Cybersecurity risk management consists of several steps including the
selection of appropriate controls to minimize risks. This is a difficult task
that requires to search through all possible subsets of a set of available
controls and identify those that minimize the risks of all stakeholders. Since
stakeholders may have different perceptions of the risks (especially when
considering the impact of threats), conflicting goals may arise that require to
find the best possible trade-offs among the various needs. In this work, we
propose a quantitative and (semi)automated approach to solve this problem based
on the well-known notion of Pareto optimality. For validation, we show how a
prototype tool based on our approach can assist in the Data Protection Impact
Assessment mandated by the General Data Protection Regulation on a simplified
but realistic use case scenario. We also evaluate the scalability of the
approach by conducting an experimental evaluation with the prototype with
encouraging results.
