  The modernization of the electrical grid and the installation of smart meters
come with many advantages to control and monitoring. However, in the wrong
hands, the data might pose a privacy threat. In this paper, we consider the
tradeoff between smart grid operations and the privacy of consumers. We analyze
the tradeoff between smart grid operations and how often data is collected by
considering a realistic direct-load control example using thermostatically
controlled loads, and we give simulation results to show how its performance
degrades as the sampling frequency decreases. Additionally, we introduce a new
privacy metric, which we call inferential privacy. This privacy metric assumes
a strong adversary model, and provides an upper bound on the adversary's
ability to infer a private parameter, independent of the algorithm he uses.
Combining these two results allow us to directly consider the tradeoff between
better load control and consumer privacy.
