  With the advent of machine learning in applications of critical
infrastructure such as healthcare and energy, privacy is a growing concern in
the minds of stakeholders. It is pivotal to ensure that neither the model nor
the data can be used to extract sensitive information used by attackers against
individuals or to harm whole societies through the exploitation of critical
infrastructure. The applicability of machine learning in these domains is
mostly limited due to a lack of trust regarding the transparency and the
privacy constraints. Various safety-critical use cases (mostly relying on
time-series data) are currently underrepresented in privacy-related
considerations. By evaluating several privacy-preserving methods regarding
their applicability on time-series data, we validated the inefficacy of
encryption for deep learning, the strong dataset dependence of differential
privacy, and the broad applicability of federated methods.
