Traditionally, risk assessment consists of evaluating the probability of "feared events", corresponding to known threats and attacks, as well as these events' severity, corresponding to their impact on one or more stakeholders. Assessing risks of cloud-based processes is particularly difficult due to lack of historical data on attacks, which has prevented frequency-based identification of "typical" threats and attack vectors. Also, the dynamic, multi-party nature of cloud-based processes makes severity assessment very dependent on the particular set of stakeholders involved in each process execution. In this paper, we tackle these problems by presenting a novel, process-oriented quantitative risk assessment methodology aimed at disclosure risks on cloud computing platforms. Key advantages of our methodology include (i) a fully quantitative and iterative approach, which enables stakeholders to compare alternative versions of cloud-based processes (e.g., with and without security controls) (ii) non-frequency-based probability estimates, which allow analyzing threats for which a detailed history is not available (iii) support for quick visual comparisons of risk profiles of alternative processes even when impact cannot be exactly quantified.
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