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.

翻译：电力消费数据非常有用,因为它可以优化电网,发现异常现象,防止故障,而且对各种研究有用。然而,电力消费数据的使用引起了重大的隐私问题,因为这些数据通常属于电力公司的客户。作为一种解决办法,我们提出一种方法来生成合成电力消费样本,忠实仿照原创,但与客户及其身份分离。我们的方法以基因反反向网络为基础。我们的贡献是双重的。首先,我们注重生成数据的质量,这不是一项微不足道的工作,因为没有标准的评价方法。然后,我们研究向神经网络培训组成员提供的隐私保障。作为隐私的最起码要求,我们要求我们的神经网络能够成为可靠的成员,进行推断攻击,因为这些攻击除了对自身隐私构成威胁之外,还为进一步攻击提供了一个通道。我们发现,在计算法提供的隐私和性能之间会有妥协。