Abstract
Load profile synthesis is a commonly used technique for preserving smart
meter data privacy. Recent efforts have successfully integrated advanced
generative models, such as the Generative Adversarial Networks (GAN), to
synthesize high- quality load profiles. Such methods are becoming
increasingly popular for conducting privacy-preserving load data
analytics. It is commonly believed that performing analyses on synthetic
data can ensure certain privacy.
In this paper, we examine this common belief. Specifically, we reveal
the privacy leakage issue in load profile synthesis enabled by GAN. We
first point out that the synthesis process cannot provide any provable
privacy guarantee, highlighting that directly conducting load data
analytics based on such data is extremely dangerous. The sample
re-appearance risk is then presented under different volumes of training
data, which indicates that the original load data could be directly
leaked by GAN without any intentional effort from adversaries.
Furthermore, we discuss potential approaches that might address this
privacy leakage issue.