Privacy-preserving Neural Networks for Smart Manufacturing

Abstract The rapid advance in sensing technology has expedited data-driven innovation in manufacturing by allowing the collection of large amounts of data from factories. Big data provides an unprecedented opportunity for smart decision-making in the manufacturing process. However, they also attract cyberattacks due to the value of sensitive information. A cyberattack on manufacturing big data can lead to a significant loss of profits and unprecedented business disruption. Moreover, the increasing use of artificial intelligence (AI) in smart factories means that manufacturing equipment is now vulnerable to cyberattacks, posing a critical threat to smart manufacturing systems. Therefore, there is an urgent need to develop AI models that incorporate privacy-preserving methods to protect sensitive information implicit in the models against model inversion attacks. Hence this paper presents the development of a new approach called Mosaic Neuron Perturbation (MNP) to preserve latent information in the framework of the AI model, ensuring differential privacy requirements while mitigating the risk of model inversion attacks. MNP is flexible to implement into AI models, enabling a trade-off between model performance and robustness against cyberattacks while being highly scalable for large-scale computing. Experimental results, based on real-world manufacturing data collected from the CNC turning process, demonstrate that the proposed method significantly improves the prevention of inversion attacks while maintaining high prediction performance. The MNP method shows strong potential for making manufacturing systems both smart and secure by addressing the risk of data breaches while preserving the quality of AI models.