  A privacy-preserving adversarial network (PPAN) was recently proposed as an
information-theoretical framework to address the issue of privacy in data
sharing. The main idea of this model was using mutual information as the
privacy measure and adversarial training of two deep neural networks, one as
the mechanism and another as the adversary. The performance of the PPAN model
for the discrete synthetic data, MNIST handwritten digits, and continuous
Gaussian data was evaluated compared to the analytically optimal trade-off. In
this study, we evaluate the PPAN model for continuous non-Gaussian data where
lower and upper bounds of the privacy-preserving problem are used. These bounds
include the Kraskov (KSG) estimation of entropy and mutual information that is
based on k-th nearest neighbor. In addition to the synthetic data sets, a
practical case for hiding the actual electricity consumption from smart meter
readings is examined. The results show that for continuous non-Gaussian data,
the PPAN model performs within the determined optimal ranges and close to the
lower bound.
