  Privacy risk assessments aim to analyze and quantify the privacy risks
associated with new systems. As such, they are critically important in ensuring
that adequate privacy protections are built in. However, current methods to
quantify privacy risk rely heavily on experienced analysts picking the
"correct" risk level on e.g. a five-point scale. In this paper, we argue that a
more scientific quantification of privacy risk increases accuracy and
reliability and can thus make it easier to build privacy-friendly systems. We
discuss how the impact and likelihood of privacy violations can be decomposed
and quantified, and stress the importance of meaningful metrics and units of
measurement. We suggest a method of quantifying and representing privacy risk
that considers a collection of factors as well as a variety of contexts and
attacker models. We conclude by identifying some of the major research
questions to take this approach further in a variety of application scenarios.
