  Physical persons playing the role of car drivers consume data that is sourced
from the Internet and, at the same time, themselves act as sources of relevant
data. It follows that citizens' privacy is potentially at risk while they
drive, hence the need to model privacy threats in this application domain. This
paper addresses the privacy threats by updating a recent threat-modelling
methodology and by tailoring it specifically to the soft privacy target
property, which ensures citizens' full control on their personal data. The
methodology now features the sources of documentation as an explicit variable
that is to be considered. It is demonstrated by including a new version of the
de-facto standard LINDDUN methodology as well as an additional source by ENISA
which is found to be relevant to soft privacy. The main findings are a set of
23 domain-independent threats, 43 domain-specific assets and 525
domain-dependent threats for the target property in the automotive domain.
While these exceed their previous versions, their main value is to offer
self-evident support to at least two arguments. One is that LINDDUN has evolved
much the way our original methodology already advocated because a few of our
previously suggested extensions are no longer outstanding. The other one is
that ENISA's treatment of privacy aboard smart cars should be extended
considerably because our 525 threats fall in the same scope.
