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Cybersecurity risk management consists of several steps including theselection of appropriate controls to minimize risks. This is a difficult taskthat requires to search through all possible subsets of a set of availablecontrols and identify those that minimize the risks of all stakeholders. Sincestakeholders may have different perceptions of the risks (especially whenconsidering the impact of threats), conflicting goals may arise that require tofind the best possible trade-offs among the various needs. In this work, wepropose a quantitative and (semi)automated approach to solve this problem basedon the well-known notion of Pareto optimality. For validation, we show how aprototype tool based on our approach can assist in the Data Protection ImpactAssessment mandated by the General Data Protection Regulation on a simplifiedbut realistic use case scenario. We also evaluate the scalability of theapproach by conducting an experimental evaluation with the prototype withencouraging results.

Identifying and Quantifying Trade-offs in Multi-Stakeholder Risk Evaluation with Applications to the Data Protection Impact Assessment of the GDPR