Quantifying the Utility-Privacy Tradeoff in the Smart Grid
This addresses privacy concerns for consumers in smart grid systems, but it is incremental as it builds on existing tradeoff studies with a new metric.
The paper tackles the tradeoff between smart grid operational utility and consumer privacy by analyzing how data sampling frequency affects load control performance and introducing a new privacy metric called inferential privacy. Simulation results show performance degradation with lower sampling, and the metric provides an upper bound on adversary inference, enabling direct tradeoff analysis.
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.