  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.
