Privacy Preservation for Machine Learning in Iiot Data Via Manifold Learning and Elementary Row Operations

Abstract

Modern large-scale production sites are highly data-driven and need large computational power due to the amount of the data collected. Hence, relying only on in-house computing systems for computational workflows is not always feasible. Instead, cloud environments are often preferred due to their ability to provide scalable and on-demand access to extensive computational resources. While cloud-based workflows offer numerous advantages, concerns regarding data privacy remain a significant obstacle to their widespread adoption, particularly in scenarios involving sensitive data and operations. This study aims to develop a computationally efficient privacy protection (PP) approach based on manifold learning and the elementary row operations inspired from the lower-upper (LU) decomposition. This approach seeks to enhance the security of data collected from industrial environments, along with the associated machine learning models, thereby protecting sensitive information against potential threats posed by both external and internal adversaries within the collaborative computing environment. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.