Evaluating Privacy Measures for Load Hiding

TL;DR

This paper evaluates various privacy measures for load hiding in smart grids, identifying the most effective one based on experiments with synthetic data and revealing that many existing measures are ineffective.

Contribution

It systematically assesses 25 privacy measures for load hiding, finding most ineffective, and identifies a mutual information variant as the most suitable for protecting appliance usage secrets.

Findings

20 of 25 privacy measures are ineffective

A mutual information variant effectively measures load hiding privacy

Synthetic data helps evaluate privacy measure effectiveness

Abstract

In smart grids, the use of smart meters to measure electricity consumption at a household level raises privacy concerns. To address them, researchers have designed various load hiding algorithms that manipulate the electricity consumption measured. To compare how well these algorithms preserve privacy, various privacy measures have been proposed. However, there currently is no consensus on which privacy measure is most appropriate to use. In this study, we aim to identify the most effective privacy measure(s) for load hiding algorithms. We have crafted a series of experiments to assess the effectiveness of these measures. found 20 of the 25 measures studied to be ineffective. Next, focused on the well-known "appliance usage" secret, we have designed synthetic data to find the measure that best deals with this secret. We observe that such a measure, a variant of mutual information, actually exists.