Privacy-Preserving Synthetic Smart Meters Data

TL;DR

This paper introduces a GAN-based method for generating synthetic power consumption data that preserves privacy by resisting membership inference attacks, balancing data quality with privacy guarantees.

Contribution

The paper presents a novel approach to generate privacy-preserving synthetic smart meter data using GANs, with a focus on data quality and robustness against membership inference attacks.

Findings

Synthetic data closely mimics real power consumption patterns

The method demonstrates robustness against membership inference attacks

A trade-off exists between data utility and privacy protection

Abstract

Power consumption data is very useful as it allows to optimize power grids, detect anomalies and prevent failures, on top of being useful for diverse research purposes. However, the use of power consumption data raises significant privacy concerns, as this data usually belongs to clients of a power company. As a solution, we propose a method to generate synthetic power consumption samples that faithfully imitate the originals, but are detached from the clients and their identities. Our method is based on Generative Adversarial Networks (GANs). Our contribution is twofold. First, we focus on the quality of the generated data, which is not a trivial task as no standard evaluation methods are available. Then, we study the privacy guarantees provided to members of the training set of our neural network. As a minimum requirement for privacy, we demand our neural network to be robust to membership inference attacks, as these provide a gateway for further attacks in addition to presenting a privacy threat on their own. We find that there is a compromise to be made between the privacy and the performance provided by the algorithm.