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Energy Resource Control via Privacy Preserving Data

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Although the frequent monitoring of smart meters enables granular control over energy resources, it also increases the risk of leakage of private information such as income, home occupancy, and power consumption behavior that can be inferred from the data by an adversary. We propose a method of releasing modified smart meter data so specific private attributes are obscured while the utility of the data for use in an energy resource controller is preserved. The method privatizes data by injecting noise conditioned on the private attribute through a linear filter learned via a minimax optimization. The optimization contains the loss function of a classifier for the private attribute, which we maximize, and the energy resource controller’s objective formulated as a canonical form optimization, which we minimize. We perform our experiment on an aggregated dataset of household consumption with solar generation and another from the Commission for Energy Regulation (CER) that contains household smart meter data with sensitive attributes such as income and home occupancy. We demonstrate on the CER data that our method is able to reduce the ability of an adversary to classify a binary income label to that of random guessing while maintaining an objective value for an energy storage controller within 10% of optimal.

Author(s):

Xiao Chen    
Stanford University
United States

Thomas Navidi    
Stanford University
United States

Ram Rajagopal    
Stanford University
United States

 

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