  Context: System Theoretic Process Analysis for Privacy (STPA-Priv) is a novel
privacy risk elicitation method using a top down approach. It has not gotten
very much attention but may offer a convenient structured approach and
generation of additional artifacts compared to other methods. Aim: The aim of
this exploratory study is to find out what benefits the privacy risk
elicitation method STPA-Priv has and to explain how the method can be used.
Method: Therefore we apply STPA-Priv to a real world health scenario that
involves a smart glucose measurement device used by children. Different kinds
of data from the smart device including location data should be shared with the
parents, physicians, and urban planners. This makes it a sociotechnical system
that offers adequate and complex privacy risks to be found. Results: We find
out that STPA-Priv is a structured method for privacy analysis and finds
complex privacy risks. The method is supported by a tool called XSTAMPP which
makes the analysis and its results more profound. Additionally, we learn that
an iterative application of the steps might be necessary to find more privacy
risks when more information about the system is available later. Conclusions:
STPA-Priv helps to identify complex privacy risks that are derived from
sociotechnical interactions in a system. It also outputs privacy constraints
that are to be enforced by the system to ensure privacy.
