Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification

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