  As IoT devices become cheaper, smaller, and more ubiquitously deployed, they
can reveal more information than their intended design and threaten user
privacy. Indoor Environmental Quality (IEQ) sensors previously installed for
energy savings and indoor health monitoring have emerged as an avenue to infer
sensitive occupant information. For example, light sensors are a known conduit
for inspecting room occupancy status with motion-sensitive lights. Light
signals can also infer sensitive data such as occupant identity and digital
screen information. To limit sensor overreach, we explore the selection of
sensor placements as a methodology. Specifically, in this proof-of-concept
exploration, we demonstrate the potential of physics-based simulation models to
quantify the minimal number of positions necessary to capture sensitive
inferences. We show how a single well-placed sensor can be sufficient in
specific building contexts to holistically capture its environmental states and
how additional well-placed sensors can contribute to more granular inferences.
We contribute a device-agnostic and building-adaptive workflow to respectfully
capture inferable occupant activity and elaborate on the implications of
incorporating building simulations into sensing schemes in the real world.
