Model Complexity as a Mitigation Strategy Against Industrial Espionage through Membership Inference Attacks

Machine learning (ML) models, particularly in the context of Federated Learning (FL), are increasingly used to enable predictive maintenance in smart industry. However, as these models become integral to industrial operations, they also become potential targets for data leakage and membership inference attacks (MIAs). In this paper, we hypothesise that training on multi-dimensional data (i.e., multiple features instead of a single feature) enhances resilience against MIAs compared to simpler, single-feature models. To test this hypothesis, we design an experimental testbed to empirically evaluate the vulnerability of ML models to black-box MIAs. Our approach involves training models of varying complexity on industrial time-series data and measuring their vulnerability to MIAs. Additionally, we introduce a human expert’s perspective to contextualise our findings in the realm of industrial espionage, emphasising the real-world implications of data leakage risks. Our results provide insights into the trade-offs between security, model complexity, and computational cost, offering practical guidance for practitioners seeking to deploy FL-based predictive maintenance solutions while minimising privacy risks.