  Fine-grained Smart Meters (SMs) data recording and communication has enabled
several features of Smart Grids (SGs) such as power quality monitoring, load
forecasting, fault detection, and so on. In addition, it has benefited the
users by giving them more control over their electricity consumption. However,
it is well-known that it also discloses sensitive information about the users,
i.e., an attacker can infer users' private information by analyzing the SMs
data. In this study, we propose a privacy-preserving approach based on
non-uniform down-sampling of SMs data. We formulate this as the problem of
learning a sparse representation of SMs data with minimum information leakage
and maximum utility. The architecture is composed of a releaser, which is a
recurrent neural network (RNN), that is trained to generate the sparse
representation by masking the SMs data, and an utility and adversary networks
(also RNNs), which help the releaser to minimize the leakage of information
about the private attribute, while keeping the reconstruction error of the SMs
data minimum (i.e., maximum utility). The performance of the proposed technique
is assessed based on actual SMs data and compared with uniform down-sampling,
random (non-uniform) down-sampling, as well as the state-of-the-art in
privacy-preserving methods using a data manipulation approach. It is shown that
our method performs better in terms of the privacy-utility trade-off while
releasing much less data, thus also being more efficient.
