  Smart building management systems rely on sensors to optimize the operation
of buildings. If an unauthorized user gains access to these sensors, a privacy
leak may occur. This paper considers such a potential leak of privacy in a
smart residential building, and how it may be mitigated through corrupting the
measurements with additive Gaussian noise. This corruption is done in order to
hide the occupancy change in an apartment. A lower bound on the variance of any
estimator that estimates the change time is derived. The bound is then used to
analyze how different model parameters affect the variance. It is shown that
the signal to noise ratio and the system dynamics are the main factors that
affect the bound. These results are then verified on a simulator of the KTH
Live-In Lab Testbed, showing good correspondence with theoretical results.
