  The explosion of data collection has raised serious privacy concerns in users
due to the possibility that sharing data may also reveal sensitive information.
The main goal of a privacy-preserving mechanism is to prevent a malicious third
party from inferring sensitive information while keeping the shared data
useful. In this paper, we study this problem in the context of time series data
and smart meters (SMs) power consumption measurements in particular. Although
Mutual Information (MI) between private and released variables has been used as
a common information-theoretic privacy measure, it fails to capture the causal
time dependencies present in the power consumption time series data. To
overcome this limitation, we introduce the Directed Information (DI) as a more
meaningful measure of privacy in the considered setting and propose a novel
loss function. The optimization is then performed using an adversarial
framework where two Recurrent Neural Networks (RNNs), referred to as the
releaser and the adversary, are trained with opposite goals. Our empirical
studies on real-world data sets from SMs measurements in the worst-case
scenario where an attacker has access to all the training data set used by the
releaser, validate the proposed method and show the existing trade-offs between
privacy and utility.
