The modernization of the electrical grid and the installation of smart meterscome with many advantages to control and monitoring. However, in the wronghands, the data might pose a privacy threat. In this paper, we consider thetradeoff between smart grid operations and the privacy of consumers. We analyzethe tradeoff between smart grid operations and how often data is collected byconsidering a realistic direct-load control example using thermostaticallycontrolled loads, and we give simulation results to show how its performancedegrades as the sampling frequency decreases. Additionally, we introduce a newprivacy metric, which we call inferential privacy. This privacy metric assumesa strong adversary model, and provides an upper bound on the adversary'sability to infer a private parameter, independent of the algorithm he uses.Combining these two results allow us to directly consider the tradeoff betweenbetter load control and consumer privacy.
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