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 involved in the process. 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.
@article{pub12143,
author = {
Mercier, Dominique
and
Lucieri, Adriano
and
Munir, Mohsin
and
Dengel, Andreas
and
Ahmed, Sheraz
},
title = {Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification},
year = {2021},
month = {11},
volume = {10},
pages = {1--9},
journal = {IEEE Transactions on Industrial Informatics (IEEE)},
publisher = {IEEE}
}
German Research Center for Artificial Intelligence Deutsches Forschungszentrum für Künstliche Intelligenz
