  Mobility data is essential for cities and communities to identify areas for
necessary improvement. Data collected by mobility providers already contains
all the information necessary, but privacy of the individuals needs to be
preserved. Differential privacy (DP) defines a mathematical property which
guarantees that certain limits of privacy are preserved while sharing such
data, but its functionality and privacy protection are difficult to explain to
laypeople. In this paper, we adapt risk communication formats in conjunction
with a model for the privacy risks of DP. The result are privacy notifications
which explain the risk to an individual's privacy when using DP, rather than
DP's functionality. We evaluate these novel privacy communication formats in a
crowdsourced study. We find that they perform similarly to the best performing
DP communications used currently in terms of objective understanding, but did
not make our participants as confident in their understanding. We also
discovered an influence, similar to the Dunning-Kruger effect, of the
statistical numeracy on the effectiveness of some of our privacy communication
formats and the DP communication format used currently. These results generate
hypotheses in multiple directions, for example, toward the use of risk
visualization to improve the understandability of our formats or toward
adaptive user interfaces which tailor the risk communication to the
characteristics of the reader.
