  Large-scale sensing and actuation infrastructures have allowed buildings to
achieve significant energy savings; at the same time, these technologies
introduce significant privacy risks that must be addressed. In this paper, we
present a framework for modeling the trade-off between improved control
performance and increased privacy risks due to occupancy sensing. More
specifically, we consider occupancy-based HVAC control as the control objective
and the location traces of individual occupants as the private variables.
Previous studies have shown that individual location information can be
inferred from occupancy measurements. To ensure privacy, we design an
architecture that distorts the occupancy data in order to hide individual
occupant location information while maintaining HVAC performance. Using mutual
information between the individual's location trace and the reported occupancy
measurement as a privacy metric, we are able to optimally design a scheme to
minimize privacy risk subject to a control performance guarantee. We evaluate
our framework using real-world occupancy data: first, we verify that our
privacy metric accurately assesses the adversary's ability to infer private
variables from the distorted sensor measurements; then, we show that control
performance is maintained through simulations of building operations using
these distorted occupancy readings.

Cryptography and Security;Systems and Control