  Smart meters (SMs) can pose privacy threats for consumers, an issue that has
received significant attention in recent years. This paper studies the impact
of Side Information (SI) on the performance of distortion-based real-time
privacy-preserving algorithms for SMs. In particular, we consider a deep
adversarial learning framework, in which the desired releaser (a recurrent
neural network) is trained by fighting against an adversary network until
convergence. To define the loss functions, two different approaches are
considered: the Causal Adversarial Learning (CAL) and the Directed Information
(DI)-based learning. The main difference between these approaches is in how the
privacy term is measured during the training process. On the one hand, the
releaser in the CAL method, by getting supervision from the actual values of
the private variables and feedback from the adversary performance, tries to
minimize the adversary log-likelihood. On the other hand, the releaser in the
DI approach completely relies on the feedback received from the adversary and
is optimized to maximize its uncertainty. The performance of these two
algorithms is evaluated empirically using real-world SMs data, considering an
attacker with access to SI (e.g., the day of the week) that tries to infer the
occupancy status from the released SMs data. The results show that, although
they perform similarly when the attacker does not exploit the SI, in general,
the CAL method is less sensitive to the inclusion of SI. However, in both
cases, privacy levels are significantly affected, particularly when multiple
sources of SI are included.
