Smart Meter Privacy: A Utility-Privacy Framework

TL;DR

This paper introduces an information-theoretic framework to balance privacy and utility in smart meter data, revealing that optimal solutions involve filtering out low-power frequency components.

Contribution

It presents a novel, minimal-assumption privacy-utility tradeoff framework for smart meters, applicable to Gaussian Markov models, unifying existing approaches.

Findings

Optimal solutions involve filtering low-power frequency components.

The framework is tractable and broadly applicable.

Most existing privacy methods are encompassed by this approach.

Abstract

End-user privacy in smart meter measurements is a well-known challenge in the smart grid. The solutions offered thus far have been tied to specific technologies such as batteries or assumptions on data usage. Existing solutions have also not quantified the loss of benefit (utility) that results from any such privacy-preserving approach. Using tools from information theory, a new framework is presented that abstracts both the privacy and the utility requirements of smart meter data. This leads to a novel privacy-utility tradeoff problem with minimal assumptions that is tractable. Specifically for a stationary Gaussian Markov model of the electricity load, it is shown that the optimal utility-and-privacy preserving solution requires filtering out frequency components that are low in power, and this approach appears to encompass most of the proposed privacy approaches.